PART III
Understanding Abatement Strategies



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution PART III Understanding Abatement Strategies

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution This page intentionally left blank.

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution 7 The Role of Monitoring and Modeling KEY POINTS IN CHAPTER 7 This chapter reviews monitoring and modeling and how each can best be used to increase understanding of coastal nutrient over-enrichment and develop management approaches. It finds: There is still great need for better technical information on status and trends in the marine environment to guide management and regulatory decisions, verify the efficacy of existing programs, and help shape national policy. Effective marine environmental monitoring programs must have clearly defined goals and objectives; a technical design based on an understanding of system linkages and processes; testable questions and hypotheses; peer review; methods that employ statistically valid observations and predictive models; and the means to translate data into information products tailored to the needs of their users, including decisionmakers and the public. There is no simple formula to ensure a successful monitoring program. Adequate resources—time, funding, and expertise—must be committed to the initial planning. The program should address all sources of variability and uncertainty, as well as cause and effect relationships. A successful monitoring program requires input from everyone who will use the data—scientists, managers, decisionmakers, and the public. Calibrated process models of estuarine water quality tend to be more useful forecasting (extrapolation) tools than simpler formulations, because they tend to include a greater representation of the physics, chemistry, and biology of the physical system being simulated. When model results are presented to managers, they should be accompanied by estimates of confidence levels.

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution Agencies should develop standards for storing and manipulating hydrologic, hydraulic, water quality, and atmospheric deposition time series. This will make it easier to link models that may not have been developed for similar purposes. Managers are often concerned with the effects of nutrient loading on commercial and recreational fisheries and other higher trophic levels. These linkages are not always clear, and the use of modeling to understand cause and effect relationships is in its infancy. The lack of knowledge about the connections among nutrient loadings, phytoplankton community response, and higher trophic levels makes modeling difficult. New models are needed that use comparative ecosystem approaches to better understand key processes and their controls in estuaries. In 1990, a major report on marine environmental monitoring (NRC 1990) concluded that: “There is a growing need for better technical information on the condition and changes in the condition of the marine environment to guide management and regulatory decisions, verify the efficacy of existing programs, and help shape national policy on marine environmental protection.” The situation has not improved dramatically in the decade since this statement was published. Environmental monitoring involves the observation or measurement of an ecosystem variable to understand the nature of the system and changes over time. Monitoring can have other important uses beyond mere observation. For instance, compliance monitoring can trigger enforcement action. In research, monitoring is used to detect interrelationships between variables and scales of variability to improve understanding of complex processes. The data acquired during monitoring can be used to specify parameters needed to create useful models and to help calibrate, verify, and evaluate models.1 When planning a monitoring program, important decisions must be made before the first observation is made, including what to measure, where to measure, when, how long, at what frequency, and which techniques to use. How these decisions are made often reflects important underlying and frequently unstated assumptions concerning how the ecosystem functions. Monitoring can play an important role in understanding and mitigating nutrient over-enrichment problems by helping pinpoint the nature and extent of problems. Because nutrient over-enrichment often results in local problems, the management responses, including monitoring programs, is typically local. This local emphasis influences the scales of 1   Calibration consists of the tuning of the model to a set of field data, preferably data that were not used in the model construction. Verification is the statistical comparison of the model output to additional data collected under different forcing and boundary conditions. Evaluation involves the comparison of model output with data collected after implementation of an environmental control program.

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution measurements; resources available to the program; decisions about what, how, and when to monitor; and the comparability of monitoring results among programs. One of the biggest challenges to effective monitoring is deciding how to allocate scarce resources. If the goal is to map a coastal characteristic with a given accuracy, then statistical techniques (e.g., Bretherton et al. 1976) provide methodologies to estimate the expected error associated with any given array of sensors. If, however, particular areas must be protected, for example a swimming beach or a fish farm, then monitoring efforts must be more focused. Unfortunately, the spatial coherence scales of eutrophication and related processes are often very small in comparison to the body of water in which they occur, with the result that what constitutes a significant variation from normal can be difficult to determine. Also, the distribution of affected areas within a given system can be patchy. When resources to support monitoring are limited, decisions concerning where to monitor may favor economically or politically sensitive regions. Typical monitoring programs are built around fixed devices and sampling schemes. To design an appropriate sampling scheme, an estimate of the important scales of variability must be made. Sampling does not need to take place at all of these scales, but, if a particular scale is not sampled, its effects must be averaged out of the record by the design of the measurement device. Otherwise, the resulting record would appear to have significant variability at scales where, in fact, it does not. Thus, through careful design, a program can conserve resources and sample only the important scales. For example, both semi-diurnal and diurnal tidal variations often affect an estuary. Nonetheless, these scales may not be the dominant scales at which eutrophication or other adverse processes take place. By averaging over a tidal cycle, the important parameters may be sampled at a lower repetition rate and still retain all the important information. Additional savings may be obtained if that sampling need not occur throughout the year. In many locations, cold temperatures, reduced metabolic rates, reduced discharge, and increased wind stirring (during certain seasons) eliminate the potential development of hypoxic conditions. If monitoring such conditions is the program goal, the monitoring may be restricted or discontinued during these seasons. Modeling and monitoring share a close interdependence. Modeling synthesizes the results of observational programs. As such, models provide important assistance for the development of monitoring arrays. Monitoring data, however, are necessary for the calibration, verification, and post-auditing (or evaluation) of models. They also provide the initial conditions, boundary conditions, and forcing functions for these models.

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution Finally, they provide data for assimilation when the models are used in a predictive mode. In these cases, real data are blended with model output to keep the model from diverging too far from reality. At least two kinds of data are necessary to run models accurately. For water quality models of receiving basins, the first category includes necessary model input parameters, such as inflows, input loads, wind vectors, hypsographic data, and tides. For watershed models, key data includes topography, precipitation, and land use characteristics. The second data category contains measured values that correspond to model output (e.g., flows, velocities, concentrations, ambient loads) for purposes of calibration, verification, and post-auditing. An iterative process of modeling, verification through careful statistical comparison of model output with observations (Willmott et al. 1985), and model modification is necessary (e.g., Herring et al. 1999) to obtain results in which managers can have confidence. Useful models require close interaction among model developers, field scientists who monitor and describe the real world, and theoreticians who explain the observations. Once quantitative measures of a model’s ability to calculate the state of the system on certain space and time scales are specified, managers can determine whether the observed level of reliability is acceptable. It can be argued that no model is truly able to predict, that is, to provide perfect estimates of future conditions. The term “predict” is used in this chapter to mean “forecast” or “estimate” for future or hypothetical conditions. The accuracy of such predictions will vary depending on the degree of integration of those who monitor with those who model. Prediction is the ultimate management use of models. While one can argue about the relative predictive skill of existing models, it is clear that prediction is an important goal justifying their development. The detail and complexity of a model is often reflected in the amount of data required to initialize and run the model. Many mathematically simple models require extensive and expensive monitoring programs to provide data before they can produce accurate results. Thus, the level of model sophistication does not necessarily indicate savings in the resources that must be devoted to monitoring in order to produce accurate hindcasts or predictions. Finally, mention should be made of the use of data assimilation. Numerical models have a tendency for their computation results to drift away from reality as they are run for longer and longer periods of time. One method used to correct this problem is to assimilate field data as they become available. If one observes a discrepancy between observations and model output, the model state is pulled back toward the observed state of the system being modeled. There exist many numerical techniques for achieving this goal. Meteorologists have used this approach

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution for many years, and physical oceanographers and biological oceanographers are beginning to incorporate it into their models. It must be remembered, however, that models are not a substitute for measurements. A properly calibrated and verified model can be useful for producing estimates of future conditions and guiding management, but field measurements, when available, are always superior to model computations. INTRODUCTION TO MONITORING Monitoring provides long-term data sets that can be used to verify or disprove existing theories developed from shorter, more focused data sets. Monitoring characterizes the scales of variability, in both space and time, thus allowing modification of sampling schemes to maximize the use of available resources. In particular, monitoring allows determination of long-term climatic scales of change, which can be mistaken for trends in shorter records. Exploratory data analysis suggests that carefully manipulated data sets from monitoring programs, along with a fair share of serendipity, may result in new insights into functional relationships among variables of an ecosystem (Tukey 1977). While this is clearly an avenue of productive future research, the number of examples of such insight remains small. Focused monitoring programs are generally established in response to, rather than in anticipation of, a problem. This means that baseline information can be missing from a monitored region. Once established, monitoring programs are useful for identifying events, but unless maintained for long periods, their utility for determining the existence of a trend is far less. In a similar sense, they are also useful for monitoring the effectiveness of remediation activities, if maintained for sufficiently long periods (i.e., periods longer than the natural scales of variability of the system). Long-term monitoring programs are necessary to isolate subtle changes in the environment. Only through data gathering programs that are sufficiently interdisciplinary in their design it will be possible to develop and test hypotheses concerning the processes and impacts of eutrophication. As pointed out by the 1990 National Research Council report Managing Troubled Waters: The Role of Marine Environmental Monitoring, monitoring is generally carried out to gather information about regulatory and permit compliance, model verification, or trends in important environmental or water quality parameters. These data can play an important role in: 1) defining the severity and extent of problems, 2) supporting

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution integrated decisionmaking when coupled with research and predictive modeling, and 3) guiding the setting of priorities for management programs. Because establishing and maintaining targeted monitoring programs is expensive and complex, greater use of environmental data collected for a variety of purposes is gaining appeal. For example, data collected through the National Pollutant Discharge Elimination System permitting process (the process of permitting point source pollutant discharges in compliance with the Clean Water Act) has demonstrated great utility for developing and evaluating the effectiveness of regional stormwater management plans and for characterizing local stormwater discharges in diverse settings (Brush et al. 1994; Cooke et al. 1994). These efforts to use data derived for regulatory purposes can provide valuable insights into the impact of land use on the concentration of a variety of constituents and thus have implications for developing loading estimates and other watershed management applications. Efforts should be made to encourage greater accessibility to similar permitting data, including associated metadata, and compliance with accepted collection and analysis protocols. Ever widening use of electronic storage and management of data sets and the greater accessibility provided by the internet hold great potential for reducing the cost of environmental monitoring by obtaining full value from data already being collected. Such a shift in philosophy, while already under way, would be facilitated if the basic guidelines and philosophies espoused herein are more fully integrated with established or contemplated regulatory monitoring plans. The committee believes that monitoring data are frequently not accessible to all who could benefit from their use (Chapter 2). Data management and the development of informational synthesis products should, therefore, be a major part of all monitoring programs—federal, state, and local. These data and syntheses should be available quickly to all users who could benefit from them at a reasonable cost. The internet offers a relatively simple, widely accessible route for distribution. ELEMENTS OF AN EFFECTIVE MONITORING PROGRAM Effective marine environmental monitoring programs must have the following features: clearly defined goals and objectives; a technical design that is based on an understanding of system linkages and processes; testable questions and hypotheses; peer review; methods that employ statistically valid observations and predictive models; and the means to translate data into information products tailored to the needs of their users, including decisionmakers and the public (NRC 1990). Monitoring programs are costly undertakings and need to be care-

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution fully planned with specific goals in mind. They often are established in response to strong public pressure, leading to situations in which program managers are expected to perform good science in a situation driven not by scientifically justifiable design but by political expediency. Legal mandates may cause duplication of effort, leave gaps in the required data records, and monitor the wrong system measures. Under the best of conditions, only a limited number of measures can be monitored for a sustained period of time. If resources are inappropriately used, the situation worsens. A related problem arises with poor sampling design. If the wrong questions are being asked, undersampling may result in not being able to sort out a weak anthropogenic signal from the natural variations in the environment. Alternatively, oversampling may result in wasted resources. Problems arise with sampling spatial scale, as well as temporal scale. Monitoring for regulatory compliance is often inappropriate for determining regional and national trends. There is no simple formula that will ensure a successful monitoring program, but much has been written on the topic over the years. In planning a monitoring system, there must be implicit decisions about how monitoring information will be used to make decisions (Box 7-1). It is imperative that all stakeholders—public, managers, policymakers, and scientists—be involved in the plan’s development, understand the implications of the various options, and agree on what results can be expected at what times in the course of the program. It is important that everyone involved harbor realistic expectations. Natural systems are complex and highly variable in time and space. Risk-free decisionmaking is an impossible goal (NRC 1990). Current monitoring programs generally do not provide integrated data across multiple natural resources at the different temporal and spatial scales needed to develop sound management policies (CENR 1997). A number of issues must be addressed in order to enhance the probability of success of future monitoring programs (NRC 1990; CENR 1997; Nowlin 1999). First, it is imperative that adequate resources—time, funding, and expertise—be committed to the initial planning of a monitoring program if the probability of success is to be maximized. These resources must be used to design a program that incorporates all sources of variability and uncertainty, as well as the best scientific understanding of cause and effect relationships. Objectives and information needs must be defined before the program design decisions can be made rationally. Successful monitoring programs strive to gather the long time series data needed for trend detection, but at the same time must be flexible to allow reallocation of resources during the program. They need to use adaptive strategies for

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution BOX 7-1 A National Coastal Monitoring Program: A Danish Example Serious signs of environmental degradation, including much publicized episodes of oxygen deficiency in the Kattegat during the 1980s, led the Danish government to create the Action Plan Against Pollution of the Danish Aquatic Environment with Nutrients in 1987. The Action Plan called for total discharges of nitrogen and phosphorus from agriculture, individual industrial outfalls, and municipal sewage works to be reduced by 50 percent and 80 percent, respectively. Because of great uncertainty about the sources of discharges as well as about the effectiveness of intervention measures, three related programs were initiated: 1) a nationwide monitoring program, 2) a marine research program, and 3) a wastewater research program. Using $1.8 million, universities, consulting firms, the Danish environmental protection agencies, and local governments created a highly effective, joint effort that has resulted in a very broad and detailed understanding of coastal nutrient over-enrichment. The Danish Nationwide Monitoring Program was undertaken to: 1) describe the quality of the aquatic environment; 2) determine where, how, and why environmental changes occur; 3) assess the effectiveness of environmental programs; and 4) determine compliance with water quality objectives. Fundamental requirements of the program were to describe geographical variation and short- and long-term temporal variation so that impacts could be identified and defined with an acceptable degree of certainty. The scope of the monitoring program was extensive and included descriptions of oxygen concentrations, marine sediments, benthic fauna, benthic vegetation, zooplankton, phytoplankton, nutrient concentration and loading, atmospheric inputs, and hydrographic conditions. Monitoring of all estuaries, bays, and coastal waters was undertaken by county governments, while open marine waters were monitored by the National Environmental Research Institute. The monitoring program incorporated data from several other national forestry, fishery, and meteorological institutes. Study results were stored in a centralized, systematic database designed to provide ready access to all potential end-users, from government officials to the public. Data and metadata are compiled and presented in a goal-oriented manner (Figure 7-1). A final component of the Action Plan was to evaluate the results of the Nationwide Monitoring Program after its completion, and then refine to the program to better meet national nutrient pollution reduction goals. The evaluation resulted in several suggestions for modifying components of the monitoring program. One important conclusion was to better couple modeling and monitoring efforts. Choice of monitoring sites often can be guided by modeling, both with respect to data requirements for model development and with respect to sites identified by the model as being sensitive to nutrient over-enrichment. Another conclusion was that

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution FIGURE 7-1 The long-term record of mean oxygen concentration under the pycnocline in the southern Skagerrak and southern Kattegat in Denmark during late summer over the period 1965-1993. Such long-term records have been collected and interpreted under the Danish Nationwide Monitoring Program and have proven to be invaluable sources of information for defining spatial and temporal scales of variability in environmental conditions and also for examining relations with variables suspected to be contributing to coastal eutrophication (modified from Christensen 1998). biological, chemical, and physical monitoring data should be coupled to obtain a better understanding of interrelationships. It was also suggested that monitoring efforts be reduced in areas that experience significant natural variation because it is difficult to demonstrate cause and effect in such places.

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution National Pollutant Discharge Elimination System permit applications for their stormwater and combined sewage. Unfortunately, much sampling data languishes in state agency or consultant files; a coordinated effort on the part of EPA is sorely needed to publish and analyze the tens of thousands of samples collected as part of the National Pollutant Discharge Elimination System permitting process. Export coefficients may be derived from event mean concentration values, if runoff volumes are known, and this is a common method for obtaining these somewhat less common parameters. Both event mean concentrations and export coefficients fit easily into spreadsheet formats for watershed loading estimates. An advantage of event mean concentrations is that they may be coupled with any hydrologic simulation model to produce loads. The committee recognizes that, especially in the urban environment, there is no coordinated effort to maintain a database of samples collected under the National Pollutant Discharge Elimination System and similar nationwide monitoring efforts. Such a database would be of inestimable value for developing loading estimates to receiving waters. Consequently, as the agency responsible for implementing National Pollutant Discharge Elimination System legislation, EPA should develop and maintain a current nationwide database of urban and other surface runoff samples for use in nonpoint source water quality analyses and modeling. Additional effort should be made to analyze such information in a manner similar to that of Driver and Tasker (1990), for purposes of developing simplified relationships between concentrations, loads, and causative factors. There are many ways to characterize watershed runoff models, such as transient versus steady-state and lumped versus distributed. Most hydrologic simulation models are transient models in the sense that they produce a hydrograph (flow versus time) that is based on a time series input of precipitation. An especially useful additional categorization of such models is whether they can generate a continuous (long-term) hydrograph (e.g., for a period of many years) or whether they are event models (e.g., just for one storm event). Continuous models (sometime referred to as period-of-record models) may use a longer time step (often one hour to correspond to hourly precipitation data) and must rely on a statistical screening of very long time series of flows and quality parameters. The output may be the basis for a frequency analysis in the absence of long-term measured data from which design events may be selected for more detailed analysis (Bedient and Huber 1992). Continuous models are especially suited to planning and frequency-based analysis since the output time series is based on historic precipitation data and is representative of climatological extremes that influence the basin and its runoff and loadings. Event models typically use more detailed schemes (i.e., the level of detail in characterizing the watershed) and shorter time steps, and

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution are executed for a single storm. The output hydrograph and pollutograph (concentration versus time) can be viewed graphically, and a model run in this mode is often used for detailed design (e.g., of a hydraulic structure or for a best management practice). Several watershed models can be run in either mode, and fast computers with extensive memory make the distinction between degree of schematization and time step less of an issue. Growing interest in the application of statistical models is taking place. Such models can vary in complexity from simple regression models such as used in the International SCOPE Nitrogen Project, to more complex models such as the the Spatially Referenced Regressions on Watersheds (SPARROW) model developed by USGS. This class of models represents useful tools for understanding the relative roles various sources may contribute to the overall nutrient load delivered to a receiving body from a complex or extremely large watershed where insufficient observational data are available to initialize or verify a process based model. ESTUARINE AND COASTAL MODELS Development of process models for estuaries and open coastal systems is still in its infancy. While it is clear that transport by both advection and diffusion is important for controlling the final distribution of nutrients and carbon in a system, existing models generally uncouple the hydrodynamics from the biological and chemical kinetics. One justification for this is that the time step necessary to accurately describe the hydrodynamics of the system is much smaller than that assumed necessary to describe the biological and chemical processes. However, this assumption has not been carefully examined for the full extent of potentially important situations, and efforts are under way to examine its validity for shallow estuaries (e.g., Inoue et al. 1996). The solution techniques used in most existing hydrodynamic models encourage acceptance of this assumption. The number of published model studies of physical-biological interactions in the coastal zone is increasing. Generally, they can be divided into two groups: (1) qualitative process models designed to increase understanding of the interactions observed in nature (e.g., Chen et al. 1997), and (2) prognostic models designed to enhance management decisions. The former often limit the size of the state space simulated (i.e., the number of independent variables). For example, many models of the lower trophic levels model generic categories termed phytoplankton, zooplankton, and nutrients. This ignores the significant differences in the interactions between subgroups of these three categories (e.g., diatoms and flagellates respond differently to various nutrient loadings and dif-

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution ferent classes of zooplankters prefer different phytoplankters as food sources). This, though, is a two-edged sword as there is evidence that increasing the number of dependent variables being modeled promotes the development of wildly varying (chaotic) solutions (Nihoul 1998). The predictive models used as management tools often are limited in the level of sophistication used in developing the dynamics that force the final biological and chemical kinetics module. On the other hand, these biological and chemical kinetics are generally more sophisticated than those appearing in the process study models. Perhaps the most challenging region to model is the continental shelf, where knowledge of the conditions along important open boundaries is generally absent. Our ability to successfully model such flow fields is limited and is only gradually improving (e.g., Herring et al. 1999). The biological variables need further differentiation, and additional chemical variables are required. The phytoplankton must be differentiated, at the very least, into diatoms and silico-flagellates. This will require the addition of a state variable for silica. The production of large versus small diatoms should influence grazing rates and efficiency. This will require development of more complex grazing models or differentiation of any zooplankton state variable. If adequate observational data exist to initialize and verify them, calibrated process models of estuarine water quality can be useful forecasting (extrapolation) tools. Under such circumstances, they have many advantages over simpler formulations (such as statistical and spreadsheet models) because they tend to include a greater representation of the physics, chemistry, and biology of the system being simulated. Statistical and spreadsheet models should be limited to use strictly in the range of their calibration. However, in appropriate settings, the simple statistical or spreadsheet approaches may be perfectly adequate. Because these simple approaches do not require large, site specific, observational data sets or complex computer facilities or expertise, they may represent a cost-effective option. Furthermore, the utility of these simple water quality models could be greatly expanded. For example, because they employ similar conceptual formulations for modeled processes, databases of typical model parameters would greatly help in application of such models (e.g., functions to describe buildup and wash-off of surface pollutants, partition coefficients, and decay coefficients) as well as for hydrologic and hydraulic parameters. Parameters used to model best management practices (e.g., removal efficiencies) are even sparser. Agencies that sponsor watershed and water quality models should also sponsor development of databases of typical modeling parameters and case studies (bibliographic databases) of modeling efforts. Such databases would facilitate the modeling of new locations.

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution Estuarine and nearshore coastal models generally consist of hydrodynamic and water quality components. Although the hydrodynamic component is generally independent of the water quality component, water quality depends on transport processes. As a result, estuarine models are classified according to the temporal and spatial complexity of the hydrodynamic component. Most existing applications of such models have been to estuarine situations. The open coastal situation is complicated theoretically by the presence of open boundaries and practically by the large domain size and consequent requirements for extensive field measurements to initialize, force, and verify the model. Notable exceptions are the efforts to model the North Sea ecosystem (Baretta et al 1995) and the Louisiana inner shelf (Bierman et al. 1994; Chen et al 1997). Significant further effort in this important area is needed. As with the watershed models described above, estuarine and coastal models can be segregated into a small set of model types (EPA 1990). The lowest level of complexity consists of desktop screening methodologies that calculate seasonal or annual mean total nutrient concentrations based on steady state conditions and simplified flushing time estimates. These models are designed for relatively simple screening-level analyses. They can also be used to highlight major water quality issues and important data gaps in the early stage of a more complex study. The next level of complexity includes numerical steady state or tidally averaged quasi-dynamic simulation models, which generally use a box or compartment-type network. Tidally averaged models simulate the net flow over a tidal cycle, and can estimate slowly changing seasonal water quality with an effective time resolution of two weeks to one month. Numerical one-dimensional and quasi-two-dimensional dynamic simulation models simulate variations in tidal heights and velocities throughout each tidal cycle. One-dimensional models treat the estuary as well mixed vertically and laterally. Quasi-two-dimensional models employ a link-node approach that describes water quality in two dimensions (longitudinal and lateral) through a network of one-dimensional nodes and channels. Tidal movement is simulated with a separate hydrodynamic package. These models are used when the data or modeling resources necessary for more complex models are unavailable. At the highest level of complexity, two-dimensional and three-dimensional dynamic simulation models, dispersive mixing and seaward boundary exchanges are treated more realistically than in one-dimensional models. These models are almost never used for

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution routine nutrient assessment, because of the high level of resources required. Although recent advances in computer design have made such models more accessible, the field data required to initialize and drive them remain impressive. OTHER RELEVANT MODELS The watershed and estuarine models discussed here can be effective in describing the transport and fate of nutrients in water. However, other models may be needed to help assess management scenarios and best management practices. Most estuarine water quality models are formulated around phytoplankton-based primary production, but in some instances it may be important to focus on macrophytes. Special models have been developed to treat such situations (Box 7-4). Within the water- BOX 7-4 Seagrass Models Process-based simulation models of seagrasses and seagrass ecosystems have been developed for a variety of reasons. Some synthesize and guide research, or identify areas of inadequate information, while others generate research hypotheses or predict the effects of nutrient additions. Recent models attempt to guide restoration efforts. Models designed to understand the effects of nutrient enrichment on seagrasses range from physiological to landscape levels. As an example, Richard Wetzel and colleagues at the Virginia Institute of Marine Sciences have been using models to understand the processes responsible for seagrass disappearance, especially in the lower Chesapeake Bay (Wetzel and Neckles 1986; Wetzel and Buzzelli 1997; Buzzelli et al. 1995, 1999; Buzzelli and Wetzel 1996; Buzzelli 1998). Through this work, researchers learned that the principal factors governing eelgrass growth and survival were largely light related. Important interactions identified in the model were epiphytes growing on plant leaves, water column particulates and phytoplankton, grazers on epiphytes, and the indirect effects of inorganic nitrogen on phytoplankton and epiphyte growth. Overall, these models have proven to be a reliable predictor of the lower Chesapeake Bay seagrass survival and depth distribution, and have been useful for testing habitat criteria in relation to restoration goals. They are presently being integrated with the Chesapeake Bay three-dimensional water quality-hydrodynamic model. The coupled seagrass-water quality model for Chesapeake Bay is a landscape-level model with certain cells focusing on the littoral zone (Wetzel 1996). The water quality model provides boundary conditions for water quality in the littoral zone and the ecosystem process models provide estimates of littoral zone transformations and exchange with the adjacent non-shoal or deeper areas represented

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution shed, biogeochemical models have been developed to describe the response of the plant community and soil to different management protocols. Such models are largely based on regression relationships, although development of models based on more fundamental relationships is progressing. CENTURY (Parton et al. 1988) is an example of such a biogeochemical model. It was developed to describe soil organic matter changes on the North American Great Plains, but has been applied to a variety of other agricultural and grassland settings with varying degrees of success (e.g., Paustian et al. 1992; Carter et al. 1993; Gijsman et al. 1996; Vallis et al. 1996; Gilmanov et al. 1997). The model considers carbon, nitrogen, phosphorus, and sulfur. Carbon is distributed among pools with different turnover rates, which depend on the plant lignin content. The time step of the original model was one month, so short-scale meteorological varia- by the water quality model. Four characteristic littoral zone habitats are represented in the model: (1) nonvegetated subtidal, (2) seagrass, (3) nonvegetated intertidal with microalgae, and (4) marshes. The relative distribution of each varies with water level and light penetration. The model has been used to address such questions as: What is the effect of halving or doubling the inorganic nutrient concentrations? What is the effect of varying open bay concentrations of phytoplankton and suspended particulate matter (an indication of eutrophication)? What is the effect of a doubling or halving the distribution of seagrass habitat on total ecosystem production, sediment microalgal production, and phytoplankton production? Other modeling efforts focus on seagrass restoration, the role of seagrasses in sediment stabilization and water column turbidity, and the linkage between seagrass habitat quality and higher trophic levels. Kremer and Deegan (J. Kremer, University of Connecticut, personal communication) are developing a model that statistically links water quality to seagrass distribution and vitality, with the goal of linking eutrophication to declines in fish community structure and productivity. The next generation of seagrass models may be particularly useful for planning restoration efforts because they address the feedback between seagrass biomass and sediment stabilization. Once seagrasses start to decline in the littoral zone, suspended solids often increase, thereby decreasing light penetration, which has a further negative impact on the distribution of remaining seagrasses. Reversing this trend through management actions directed at nutrient reduction will also have to address the issue of sediment stabilization and water clarity.

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution tions were missed. Furthermore, the original model did not include runoff, although this process could be added with additional effort (e.g., Probert et al. 1995). More recent developments (Parton et al. 1998) have shortened the model time step to one day, and added runoff processes and multiple layers to the soil model. The original model reproduces multi-year trends in soil nitrogen change and annual statistics, although events are frequently missed. The recent improvements reproduce short-term variability on some occasions and fail to reproduce reality on others (Parton et al. 1998). They clearly need further development. Sixteen such models, applied to a long-term study of a forest-soil-atmosphere system, are described by Tiktak and van Grinsven (1995). They conclude that most such models are poorly documented for community use and include significant simplification, such as ignoring dynamic feedbacks, ignoring short time scale events, aggregating state variables, simplifying and ignoring boundary conditions, and simplifying and ignoring processes. Furthermore, many of the models were unbalanced in their treatment of the processes included. Of significance to the present discussion is the lack of an agreement among the models as to description of the nutrient cycle. Tiktak and van Grinsven (1995) recommended the development of lumped parameter models suitable for scenario assessment, but, because the necessary data are not available to verify such models, they need to be compared to detailed mechanistic models. The development of the latter was strongly recommended. Nitrate modeling by 11 of these models (Kros and Warfvinge 1995) was not very successful despite the complexity of approaches used. None of the models could reproduce the seasonal soil nitrate variations and only two could reproduce observed extreme values. These failures were attributed to soil heterogeneity, unresolved litter layer hydrologic processes, and complex microbial transformations. Some of these models estimate the loss of nitrogen to the atmosphere as trace gases. While the different models adequately reproduce the flux of N2O, they fail to accurately predict the flux of NO or NH3 (Frolking et al. 1998), the gases that are most important in driving nitrogen deposition on coastal watersheds. The committee recommends that biogeochemical models be developed because they are so important for understanding a watershed’s response to best management practices and other proposed management scenarios. This is proposed even though this type of model has not yet performed at a uniformly reliable level in all environments (e.g., forests, plains, and agricultural fields). Detailed mechanistic models must be developed for comparison with field results and used to develop simple calibrated lumped-parameter models for regional analyses. Because of the importance of atmospheric deposition of nitrogen to

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution coastal eutrophication, it would be helpful to have models of the atmospheric transport and deposition of this nutrient available to managers. While a number of such models exist, it seems that they must be calibrated to particular regions of the globe to accurately predict deposition (Holland et al. 1999), and that their spatial resolution is so large that deposition at the scales of ecosystems is poorly resolved (Holland et al. 1997). For example, as discussed in Chapter 5, the ability of forests to store nitrogen is limited. Once a forest is saturated with respect to nitrogen, losses both to the atmosphere and to downstream ecosystems can increase rapidly. Some evidence indicates that the process whereby forests switch from retaining nitrogen to exporting nitrogen as they become nitrogen saturated can be self-accelerating due to related changes in biogeochemical cycling and ecosystem decline (Schulze et al. 1989; Howarth et al. 1996). Successfully capturing such complex ecosystem behavior through time should be an important component of efforts to develop the next generation of process-based models. A related issue is linking models that were developed for different purposes. For instance, it is not necessarily a simple task to input a time series of atmospheric deposition values into a watershed loading model or a receiving water quality model. Nor is it straightforward to input the time series of flows and loadings from a watershed model into a receiving water model. To facilitate interfacing of different models, EPA or other relevant agencies should develop standards for storage and manipulation of hydrologic, hydraulic, and water quality time series. This will make it much easier to link models that may not have been developed for similar purposes, but may usefully provide input from one to another. Since people are so important a component of the environment contributing to eutrophication, it will be important to incorporate socioeconomic variables into models that purport to predict landscape variability and its results. Initial efforts in this direction are being made using transition probability matrixes for future land use based on socioeconomic indicators (e.g., Berry et al. 1996). The effectiveness of such models has yet to be demonstrated. At present, the best approach to account for longterm changes in climate, land use, and related factors is to run the same model under different scenarios or forcing. This is similar to running coupled global ocean-atmosphere models under the assumption of doubled atmospheric carbon dioxide content to infer the potential system response to continued fossil fuel burning. RECOMMENDATIONS How should a prospective modeler select models? As can be seen from the models highlighted in Appendix D, there are few federal agency

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution sources of useful off-the-shelf models. For watershed models, these are found primarily at USDA, EPA, USGS, and the U.S. Army Corps of Engineers’ Hydrologic Engineering Center. For receiving water quality models, EPA, and the U.S. Army Corps of Engineers’ Waterways Experiment Station are the primary federal sources. Most software provided by federal agencies is essentially free, but may include minimum costs for support and training. All these models are supplemented by proprietary and other software developed by American and European companies and universities. Proprietary models may be relatively costly ($5,000 to $30,000), but typically they include sophisticated interfaces and extensive user support. In some cases, models may only be available through the purchase of the services of the model developer. Thus, a client may decide on a model based as much on the qualifications of the sponsoring firm or agency as on the properties of the software. Because modeling continues to be partially an art, the expertise and experience of the modeler is of consequence. It is a good idea to rely on competent technical personnel and allow them to decide on the choice of a model. If the model will be provided to a particular manager for his long-term use, then the model itself must be well documented and understandable by its future users. Data requirements also enter into the choice of a model, since some process models may require more information than can be affordably provided. This tends to drive the model selection toward less sophisticated techniques that require fewer data. Verification and checks for reasonableness of estimates provided by simple models are especially important in order to lend credibility to such estimates. Since model choices remain somewhat limited, a manager may be tempted to develop an in-house model. While this option may be feasible, in-house models tend to be specific to individual problems and locations and are seldom subject to peer review and the experience of a variety of users. Apart from simple statistical or spreadsheet models, this approach is usually not cost effective relative to acquisition of federal models or use of the services of model providers. It is clear that a number of numerical modeling codes of varying degrees of sophistication are available to the scientist and manager interested in eutrophication. While many of these models are accepted and used successfully in general practice, the level of quantitative verification, post-audit, and skill assessment that has been applied to them is highly variable. Most have only been subjected to qualitative comparison to field data sets. In many cases, these models probably overestimate nutrient inputs prior to European settlement. As a result, they underestimate the extent to which human activity has accelerated inputs of nutrients. Development of fully three-dimensional, verified, and skill-assessed water quality models should be encouraged for synthesis and management.

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution This implies that well designed monitoring programs be effectively linked to the iterative development of models so that both the data and model syntheses can be used in management decisions and policy. It is expected, however, that input parameter estimates for process models should also fall within accepted ranges for such values. Verification and checks for reasonableness of estimates provided by simple models are especially important for lending credibility to such estimates. Whenever possible, uncertainty in the model output should be represented (e.g., as a mean plus a standard deviation) or as confidence limits on the output of a time series of concentrations or flows. To facilitate interfacing of different models, EPA, USGS, or other relevant agencies should develop standards for storage and manipulation of hydrologic, hydraulic, water quality, and atmospheric deposition time series. This will make it much easier to link models that may not have been developed for similar purposes, but may usefully provide input from one to another. Models are excellent tools for synthesis of our understanding of systems. As management tools, they can only begin to describe the variables specified per se in the model. Such management concepts as sustainable, healthy ecosystems are not quantifiable and cannot be a variable predicted from a numerical model. Great care must be given to identifying the appropriate parameters to estimate and the measures to be applied to these parameters (Huggins 1963). The assumptions that enter into this definitional process are often as important and interesting as the science involved. All models benefit from continuous improvement. Federal agency support for widely used models that the agency has developed or sponsored should include maintenance, improvements, help with parameter estimates, and a feedback mechanism. The latter could conveniently be accomplished through discussion groups on the internet. The numerical values of the often very large number of required input parameters are as important as the model formulations themselves. Data sets of input parameters for calibrated models should be provided by the agencies charged with oversight of the models. Agencies that sponsor watershed and water quality models should also sponsor development of databases of typical modeling parameters and a bibliographic database of case studies of model applications. Such databases would enormously ease the effort in modeling new locations. Agencies responsible for monitoring of parameters useful for calibration and verification of models should also develop and maintain databases containing this information. For example, as the agency responsible for implementing the National Pollution Discharge Elimination System legislation, EPA should develop and maintain a current nationwide data-

OCR for page 195
Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution base of urban and other surface runoff samples collected during this program for use in nonpoint source water quality analyses and modeling. Additional effort should be devoted to characterization and statistical analysis of such data. Managerial concern with the impacts of nutrient over-enrichment often is concerned with the perceived effects that nutrient loading will have on higher trophic levels in the system (e.g., the loss of commercial and recreational fisheries). These linkages are not always clearly demonstrable, and modeling of such cause and effect relationships is in its infancy. To further complicate the situation, the phytoplankton appear to be readily modeled as a continuum, while higher trophic levels often are characterized using individual models. The European regional seas ecosystem model (Baretta et al. 1995) is a suite of interconnected models that attempt to model the entire North Sea ecosystem up through the fish communities and including the benthos and the microbial loop. The present lack of knowledge concerning the connections among nutrient loadings, phytoplankton community response, and higher trophic levels implies a disconnection between the estimates used to evaluate management scenarios and the goals for which management is taking place. Therefore, the development of heuristic models using comparative ecosystem approaches is needed to identify and better understand key processes and their controls in estuaries.