Hypothesis Testing by Simulation: An Environmental Example

The study of environmental systems as ecological, physico-chemical as well as socio-economic entities requires a high degree of simplifying formalism. However, a detailed understanding of a systems function and response to various changes for the explicit purpose of systems management and planning, still requires complex hypotheses, or models, which can hardly be subjected to rigorous tests without the aid of computers. Systems simulation is a powerful tool when subjecting complex hypotheses to rigorous tests of their logical structure, as well as a possible means for rejecting or corroborating the underlying hypotheses. The complexity and variability of environmental systems, the scarcity of appropriate observations and experiments, problems in the interpretation of empirical data, and the lack of a well established theoretical background make it difficult to test any possible conceptualization, or hypothesis, describing a given system. A formal approach to hypothesis testing, based on numerical simulation, which explicitly considers the above constraints, is proposed. Based on a data set from the North Sea, a series of hypotheses on the structural relations and the dynamic function of the pelagic food web is formulated in terms of numerical models. Hypotheses of various degrees of aggregation and abstraction are tested by comparing singular statements (predictions) deduced from the proposed hypotheses (the models) with the observations. The basic processes of primary production, consumption, and remineralization, driven by light, temperature, and advection/diffusion, are described in systems models ranging in complexity from two compartments to many compartments and species groups. With each of the proposed models, yearly cycles of the systems behavior are simulated. A comparative analysis of the response of each of the models allows conclusions to be drawn on the adequacy of the alternative hypotheses. This analysis also allows one to reject inadequate constructs, and provides some guidance on how to improve a certain hypothesis, even in the presence of a high degree of uncertainty

Modelling Biological Processes in the Aquatic Environment

Based on some of the most recent contributions to the field of biological modelling within the frame of ecosystems analysis, some aspects of modelling eco-physiological processes in the aquatic environment are discussed. First, a few rather general comments are made on the predictive capabilities of complex ecosystems models, and the related need to use more realistic and causal descriptions for various complex biological processes. Following this, some ideas and formulations, guided by the above principles, are compiled and discussed. The use of more realistic representations of biological processes, including time-varying parameters, is advocated, and several approaches are compared. Key factors such as temperature, light or nutrients are considered with regard to the basic biological internal control mechanism of adaptation. The inclusion of adaptation phenomena in the representation of, for example, effects of temperature, light dependency of primary production, or nutrient uptake kinetics, is described on different levels of mechanistic detail and complexity, and as a holistic feature. This is also an attempt to reduce dimensionality in complex models by increasing the realism in the description of functionally heterogeneous lumped compartments and thus avoiding separate detailed descriptions of their major component elements. In addition to the adaptation in single-species populations, the problem of community adaptation in multi-species populations, represented in most ecosystem models by lumped variables and averaged parameters, is considered in relation to environmental fluctuations and environmental uncertainty. A concept of environmental tracking is proposed, represented by the relation of parameter values to their governing input variables and state variables, as a major adaptive strategy for biotic systems

Estimating Model Prediction Accuracy: A stochastic Approach to Ecosystem Modeling

Ecosystems are, as a rule, characterized by a large behavioral repertoire showing a high degree of structural variability and complex control mechanisms such as adaptation and self-organization. Our quantitative understanding of ecosystems behavior is generally poor, and field data are notoriously scarce, scattered, and noisy. This is most pronounced on a high level of aggregation where considerable sampling errors are involved. Also, no well established and generally accepted ecological theory exists, so that an operational ecosystem model consists of many more arbitrary, simplifying assumptions (more often than not implicitly hidden in process descriptions) than properties measurable in the field. Consequently, predictions of future systems behavior under changed conditions -- a most desirable tool for environmental management -- cannot be precise and unique in a deterministic sense. Rather, it is essential to estimate the levels of model reliability and the effects of various sources of uncertainty on model prediction accuracy. A concept of allowable ranges for model data-input and expected model response, explicitly including uncertainty in the numerical methods, is proposed. Straightforward Monte Carlo simulation techniques are used, and the approach is exemplified on a lake ecosystem eutrophication problem. The method attempts to predict future systems states in terms of probability distributions, and explores the relations of prediction accuracy to data uncertainty and systems variability, the time horizon of the prediction, and finally the degree of extrapolation in state- and input-space relative to the empirical range of systems behavior. The analysis of almost 100,000 model runs also allows some conclusions on model sensitivity, and some desirable model properties in light of prediction accuracy are identified

Advanced Decision-Oriented Software for the Management of Hazardous Substances: Part III - Decision Support and Expert Systems: Uses and Users

Many industrial processes, products, and residuals such as hazardous and toxic substances, pose risks to man and are harmful to the basic life-support system of the environment. In order to reduce risks to individuals and society as a whole, and to ensure a sustainable use of the biosphere for present and future generations, it is imperative that these substances are managed in a safe and systematic manner. The aim of this project is to provide software tools which can be used by those engaged in the management of the environment, industrial production, products, and waste streams, and hazardous substances and wastes in particular. This set of tools is designed for a broad group of users, including non-technical users. Its primary purpose is to improve the factual basis for decision making, and to structure the decision-making process in order to make it more consistent, by providing easy access and allowing efficient use of methods of analysis and information management which are normally restricted to a small group of technical experts

Advanced Decision-Oriented Software for the Management of Hazardous Substances. Part VI: The Interactive Decision-Support Module

In this paper we introduce an interactive, display-oriented postprocessor for multiobjective selection or discrete optimization, which has been implemented within the framework of a project on Advanced Decision-Oriented Software for the Management of Hazardous Substances. The approach and software described here is designed as a tool to improve the usefulness and usability of decision support systems through the easy access to a rich set of powerful support functions and display options, and tight integration with substantive models and data bases. At the same time it adds a new dimension of usefulness to the simulation models it is connected to as an output post-processor, aiding in the comparative evaluation of complex modeling results

Expert Systems for Environmental Screening. An Application in the Lower Mekong Basin

This research report describes MEXSES, a rule-based expert system for environmental impact assessment at a screening level, implemented for the analysis of water resources development projects in the Lower Mekong Basin. The report makes a brief review of environmental impact assessment methods and tools. It discusses expert systems technology, with emphasis on environmental applications. The Lower Mekong Basin and its specific environmental problems, as well as the Mekong Secretariat's environmental policy are examined. Subsequently, the software system is described from a user's perspective, followed by a detailed description of the methodology employed and its implementation. In the final chapter, a number of issues around the successful application of such a system are discussed, including a number of suggested improvements and extensions to the current operational prototype

Interactive Water Quality Simulation in a Regional Framework: A Management Oriented Approach to Lake and Watershed Modeling

A phosphorus-based water quality simulation model for a shallow lake/reed system (Neusiedlersee, Austria) has been coupled with a series of heuristic, process oriented models describing the physical and socio-economic catchment of the lake. Built into an interactive, modular decision support system's framework, which includes simple database management, interactive video graphics, and linguistic output formats, the programs are designed for the simulation and evaluation of regional development, i.e., agricultural landuse, industry, tourism, wastewater treatment, and perceived lake water quality. Special emphasis is put on communication and display through a friendly man-machine interface, including linguistic statements for the description of lake water quality

Advanced Decision-Oriented Software for the Management of Hazardous Substances: Part II: A Demonstration Prototype System

This report describes the implementation of a first demonstration prototype of an integrated, interactive, computer-based decision support and information system for the management of hazardous substances. Recognizing the potentially enormous development effort required and the open-ended nature of such a project, we have opted for a strategy that takes advantage of the large volume of scientific software already available. A modular design philosophy enables us to develop individual building blocks, which are valuable products in their own right, in the various phases of the project. This also makes it possible to interface and integrate the modules in a framework which, above all, has to be flexible and easily modifiable with growing experience of use. The demonstration prototype can be constructed at relatively low cost and only incremental effort, by using an open architecture concept for this framework, with a functional and problem-oriented, rather than a structural and methodological design. The system design combines several methods of applied systems analysis and operations research, planning and policy sciences, and artificial intelligence into one fully integrated software system. The basic objective is to provide a broad group of users direct and easy access to these largely formal and complex methods

Environmental Modeling Under Uncertainty: Monte Carlo Simulation

The study of environmental systems as ecological and physicochemical as well as socioeconomic entities requires a high degree of simplifying formalism. However, a detailed understanding of a systems function and response to various changes for the explicit purpose of systems management and planning still requires fairly complex hypotheses, or models. Such models can hardly be subjected to rigorous tests without the aid of computers. Systems simulation is a powerful tool when subjecting complex hypotheses to critical tests of their logical structure and their performance over the range of plausible input conditions . Based on a formalized trial-and-error approach using Monte Carlo methods, this report presents and discusses an approach to simulation modeling under uncertainty. An introduction to the causes and implications of the problem, namely uncertainty, and a short formal presentation of the methodology proposed are followed by some more technical remarks on Monte Carlo simulation. Using three different application examples, the author discusses the role of uncertainty in the formal testing of model structures, in parameter estimation, and in prediction. In the last example, the limits of estimation and, with it, prediction are demonstrated. In a comparison of Monte Carlo simulation and alternative approaches to including and evaluating uncertainty in simulation modeling, the discussion section examines the implications of uncertainty for model application in a broader framework

Uncertainity and Arbitrariness in Ecosystems Modelling: A Lake Modelling Example

Mathematical models of ecosystems are considerable simplifications of reality, and the data upon which they are based are usually scarce and uncertain. Calibration of large, complex models depends upon arbitrary assumptions and choices, and frequently calibration procedures do not deal adequately with the uncertainty in the data describing the system under study. Since much of the uncertainty and arbitrariness in ecological modeling is inevitable, because of both practical as well as theoretical limitations, model-based predictions should at least reveal their dependence on, and sensitivity to, uncertainty and arbitrary assumptions. The paper proposes a method that explicitly takes into account the uncertainty associated with data for modeling. By reference to a qualitative and somewhat vague definition of system behavior in terms of allowable ranges, an ensemble of acceptable parameter vectors for the model may be identified. This contrasts directly with a more conventional approach to model calibration, in which a quantitative (squared-error) criterion is minimized and through which a supposedly "unique" and "best" set of parameters can be derived. The ensemble of parameter vectors is then used for the simulation of a multitude of future systems behavior patterns, so that the uncertainty in the initial data and assumptions is preserved, and the thus predicted future systems response can be interpreted in a probabilistic manner