Kenneth H. Reckhow

Water quality simulation modeling and uncertainty analysis for risk assessment and decision making

The usefulness of water quality simulation models for environmental management is explored with a focus on prediction uncertainty. Ecological risk and environmental analysis often involve scientific assessments that are highly uncertain. Still, environmental management decisions are being made, often with the support of a mathematical simulation model. In the area of pollutant transport and fate in surface waters, few of the extant simulation models have been rigorously evaluated. Limited observational data and limited scientific knowledge are often incompatible with the highly-detailed model structures of the large pollutant transport and fate models. Two examples are presented to illustrate data and knowledge weaknesses that are likely to undermine these large models for decision support. An alternative to comprehensive structured simulation models is proposed as a flexible approach to introduce science into the environmental risk assessment and decision making process.

A bayesian hierarchical model to predict benthic oxygen demand from organic matter loading in estuaries and coastal zones

Ecological models that have a theoretical basis and yet are mathematically simple enough to be parameterized using available data are likely to be the most useful for environmental management and decision-making. Mechanistic foundations improve confidence in model predictions, while statistical methods provide empirical support for parameter selection and allow for estimates of predictive uncertainty. However, even models that are mechanistically simple can be overparameterized when system-specific data are limited. To overcome this problem, models are often fit to data sets composed of observations from multiple systems. The resulting parameter estimates are then used to predict changes within a single system, given changes in management variables. However, the assumption of common parameter values across all systems may not always be valid. This assumption can be relaxed by adopting a hierarchical approach. Under the hierarchical structure, each system has its own set of parameter values, but some commonality in values is assumed across systems. An underlying population distribution is employed to structure this commonality among parameters, thereby avoiding the problems of overfitting. The hierarchical approach is, therefore, a practical compromise between entirely site-specific and globally-common parameter estimates. We applied the hierarchical method to annual data on organic matter loading and benthic oxygen demand from 34 estuarine and coastal systems. Both global and system-specific parameters were estimated using Bayes Theorem. Compared to the global model, the hierarchical model results in predictions of oxygen demand that more accurately represent site-specific observation but are less precise than the global model. Lower precision occurs because, by allowing each system to have its own parameter values, we effectively reduce the amount of information we have to estimate those parameters. However, if, by permitting model parameters to differ by location, the hierarchical model is believed to be more realistic than the global model, then the lower precision represents a more proper translation of our knowledge into predictions. Appropriate representation of prediction precision can have important implications for management intended to reduce oxygen depletion. Depending on the predictive precision resulting from the availability and nature of site-specific data, the hierarchical model may suggest more or less stringent organic matter loading rates than a model assuming global parameter commonality. The generality of the hierarchical approach makes it suitable for a number of ecological modeling applications in which cross-system data are required for empirical parameter estimation, yet only partial commonality can be assumed across sampling units.

Importance of scientific uncertainty in decision making

Uncertainty in environmental decision making should not be thought of as a problem that is best ignored. In fact, as is illustrated in a simple example, we often informally make use of awareness of uncertainty by hedging decisions away from large losses. This hedging can be made explicit and formalized using the methods of decision analysis. While scientific uncertainty is undesirable, it can still be useful in environmental management as it provides a basis for the need to fund additional monitoring, experimentation, or information acquisition to improve the scientific basis for decisions.

Stakeholder Values and Scientific Modeling in the Neuse River Watershed

In 1998, the North Carolina Legislature mandated a 30% reduction in the nitrogen loading in the Neuse River in an attempt to reduce undesirable environmental conditions in the lower river and estuary. Although sophisticated scientific models of the Neuse estuary exist, there is currently no study directly relating the nitrogen-reduction policy to the concerns of the estuarine systems stakeholders. Much of the difficulty lies in the fact that existing scientific models have biophysical outcome variables, such as dissolved oxygen, that are typically not directly meaningful to the public. In addition, stakeholders have concerns related to economics, modeling, implementation, and fairness that go beyond ecological outcomes. We describe a decision-analytic approach to modeling the Neuse River nutrient-management problem, focusing on linking scientific assessments to stakeholder objectives. The first step in the approach is elicitation and analysis of stakeholder concerns. The second step is construction of a probabilistic model that relates proposed management actions to attributes of interest to stakeholders. We discuss how the model can then be used by local decision makers as a tool for adaptive management of the Neuse River system. This discussion relates adaptive management to the notion of expected value of information and indicates a need for a comprehensive monitoring program to accompany implementation of the model. We conclude by acknowledging that a scientific model cannot appropriately address all the stakeholder concerns elicited, and we discuss how the remaining concerns may otherwise be considered in the policy process.

On the application of multilevel modeling in environmental and ecological studies

This paper illustrates the advantages of a multilevel/hierarchical approach for predictive modeling, including flexibility of model formulation, explicitly accounting for hierarchical structure in the data, and the ability to predict the outcome of new cases. As a generalization of the classical approach, the multilevel modeling approach explicitly models the hierarchical structure in the data by considering both the within- and between-group variances leading to a partial pooling of data across all levels in the hierarchy. The modeling framework provides means for incorporating variables at different spatiotemporal scales. The examples used in this paper illustrate the iterative process of model fitting and evaluation, a process that can lead to improved understanding of the system being studied.

Bayesian Inference in Non‐Replicated Ecological Studies

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Confirmation of water quality models

Abstract Water quality simulation models, whether descriptive or predictive, must undergo confirmatory analyses if inferences drawn from the models are to be meaningful. Current practices in the confirmation of simulation models are examined and criticized from this perspective. In particular, labeling this process “verification” or “validation” (truth) probably contributes to the often inadequate efforts, since these states are unattainable. The evaluation of scientific hypotheses, or water quality simulation models, may proceed according to inductive logic, the hypothetico-deductive approach, or perhaps according to a falsification criterion. The result of successful testing is at best confirmation or corroboration, which is not truth but rather measured consistency with empirical evidence. On this basis a number of statistical tests are suggested for model confirmation. The major difficulty to overcome, before confirmation becomes meaningful, is the generally inadequate data for establishing rigorous statistical tests.

Modeling excessive nutrient loading in the environment

Models addressing excessive nutrient loading in the environment originated over 50 years ago with the simple nutrient concentration thresholds proposed by Sawyer (1947. Fertilization of lakes by agricultural and urban drainage. New Engl. Water Works Assoc. 61, 109-127). Since then, models have improved due to progress in modeling techniques and technology as well as enhancements in scientific knowledge. Several of these advances are examined here. Among the recent approaches in modeling techniques we review are error propagation, model confirmation, generalized sensitivity analysis, and Bayesian analysis. In the scientific arena and process characterization, we focus on advances in surface water modeling, discussing enhanced modeling of organic carbon, improved hydrodynamics, and refined characterization of sediment diagenesis. We conclude with some observations on future needs and anticipated developments.

The use of Bayesian networks for nanoparticle risk forecasting: Model formulation and baseline evaluation:

We describe the use of Bayesian networks as a tool for nanomaterial risk forecasting and develop a baseline probabilistic model that incorporates nanoparticle specific characteristics and environmental parameters, along with elements of exposure potential, hazard, and risk related to nanomaterials. The baseline model, FINE (Forecasting the Impacts of Nanomaterials in the Environment), was developed using expert elicitation techniques. The Bayesian nature of FINE allows for updating as new data become available, a critical feature for forecasting risk in the context of nanomaterials. The specific case of silver nanoparticles (AgNPs) in aquatic environments is presented here (FINE(AgNP)). The results of this study show that Bayesian networks provide a robust method for formally incorporating expert judgments into a probabilistic measure of exposure and risk to nanoparticles, particularly when other knowledge bases may be lacking. The model is easily adapted and updated as additional experimental data and other information on nanoparticle behavior in the environment become available. The baseline model suggests that, within the bounds of uncertainty as currently quantified, nanosilver may pose the greatest potential risk as these particles accumulate in aquatic sediments.

An evaluation of automated structure learning with Bayesian networks: An application to estuarine chlorophyll dynamics

We develop a Bayesian network (BN) model that describes estuarine chlorophyll dynamics in the upper section of the Neuse River Estuary in North Carolina, using automated constraint based structure learning algorithms. We examine the functionality and usefulness of the structure learning algorithms in building model topology with real-time data under different scenarios. Generated BN models are evaluated and a final model is selected. Model results indicate that although the effect of water temperature and river flow on chlorophyll dynamics has remained unchanged following the implementation of the nitrogen Total Maximum Daily Load (TMDL) program; the response of chlorophyll levels to nutrient concentrations has been altered. The results stress the importance of incorporating expert defined constraints and links in conjunction with the automated structure learning algorithms to generate more plausible structures and minimize the sensitivity of the learning algorithms. This hybrid approach towards structure learning allows for the incorporation of existing knowledge while limiting the scope of the learning algorithms to defining the links between environmental variables for which the expert has little or no information.