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Simulation modeling is arguably the most versatile scientific tool for predicting the future environment. However, the reliability of model-based predictions is limited to the behavior domain defined by the historical data employed for conceptualizing and calibrating the model. Future changes in external inputs and internal structure tend to produce system behavior significantly different from prior predictions. To abate this seeming lack of credibility, it is now customary to qualify model predictions with uncertainty estimates. This dissertation explores the complementary approach of back-casting future scenarios. Centered on the analysis of uncertainty, a methodological framework is developed for the computational evaluation of environmental futures, driven by stakeholder participation as a means for establishing credibility in the model. The analysis reveals possible structural change between the observed past and speculated future scenarios by comparing the ranking of key sources of uncertainty in model outputs. Three sampling-based methods are employed: Regionalized Sensitivity Analysis (RSA), Tree-Structured Density Estimation (TSDE), and Uniform Covering by Probabilistic Rejection (UCPR). RSA and TSDE are tested for identifying and ranking the key factors that influence ecological behavior in Lake Oglethorpe, Georgia, and UCPR, for recovering parameters of a rainfall-runoff model of an experimental watershed near Loch Ard, Scotland. The framework is applied to an integrated assessment of ecological behavior in Lake Lanier, Georgia. Stakeholders' fears and desires for the future state of the reservoir are elicited and encoded for analysis. The results indicate: (i) that the desired future is more reachable, and accompanied by more significant structural change, than the feared future, and (ii) that sediment-water-nutrient interactions, secondary production, and microbial processes play a critical role in the future ecological behavior of the reservoir. Thus, it is possible to: (i) confirm or refute stakeholder concerns for the future environment, (ii) inform priorities for future environmental policy actions, (iii) identify critical gaps in current knowledge, in order to prioritize future scientific research, and (iv) promote adaptive community learning, through the continual mutual feedback between scenario-generation and systematic analysis. By bridging the gap between stakeholder imagination and scientific theory, through computational analysis, the framework provides a promising direction for integrated environmental assessment.
Policy-makers and the public, it has famously been said, are more interested in the possibility of non-linear dislocations and surprises in the behaviour of the environment than in smooth extrapolations of current trends. The International Task Force in Forecasting Environmental Change (1993-1998) dedicated its work to developing procedures of model building capable of addressing our palpable concerns for substantial change in the future. This volume discusses the immense challenges that such structural change presents - that the behaviour of the environment may become radically different from that observed in the past - and investigates the potentially profound implications for model development.Drawing upon case histories from the Great Lakes, acidic atmospheric deposition and, among others, the urban ozone problem, this discourse responds to a new agenda of questions. For example: "What system of 'radar' might we design to detect threats to the environment lying just beyond the 'horizon'?" and "Are the seeds of structural change identifiable within the record of the recent past?"Meticulously researched by leading environmental modellers, this milestone volume engages vigorously with its subject and offers an animated account of how models can begin to take into consideration the significant threats and uncertainties posed by structural change.
The complex and multidisciplinary nature of environmental problems requires that they are dealt with in an integrated manner. Modeling and software have become key instruments used to promote sustainability and improve environmental decision processes, especially through systematic integration of various knowledge and data and their ability to foster learning and help make predictions. This book presents the current state-of-the-art in environmental modeling and software and identifies the future challenges in the field. - State-of-the-art in environmental modeling and software theory and practice for integrated assessment and management serves as a starting point for researchers - Identifies the areas of research and practice required for advancing the requisite knowledge base and tools, and their wider usage - Best practices of environmental modeling enables the reader to select appropriate software and gives the reader tools to integrate natural system dynamics with human dimensions
This volume brings together, in a central text, chapters written by leading scholars working at the intersection of modeling, the natural and social sciences, and public participation. This book presents the current state of knowledge regarding the theory and practice of engaging stakeholders in environmental modeling for decision-making, and includes basic theoretical considerations, an overview of methods and tools available, and case study examples of these principles and methods in practice. Although there has been a significant increase in research and development regarding participatory modeling, a unifying text that provides an overview of the different methodologies available to scholars and a systematic review of case study applications has been largely unavailable. This edited volume seeks to address a gap in the literature and provide a primer that addresses the growing demand to adopt and apply a range of modeling methods that includes the public in environmental assessment and management. The book is divided into two main sections. The first part of the book covers basic considerations for including stakeholders in the modeling process and its intersection with the theory and practice of public participation in environmental decision-making. The second part of the book is devoted to specific applications and products of the various methods available through case study examination. This second part of the book also provides insight from several international experts currently working in the field about their approaches, types of interactions with stakeholders, models produced, and the challenges they perceived based on their practical experiences.
Simulation models are an established method used to investigate processes and solve practical problems in a wide variety of disciplines. Central to the concept of this second edition is the idea that environmental systems are complex, open systems. The authors present the diversity of approaches to dealing with environmental complexity and then encourage readers to make comparisons between these approaches and between different disciplines. Environmental Modelling: Finding Simplicity in Complexity 2nd edition is divided into four main sections: An overview of methods and approaches to modelling. State of the art for modelling environmental processes Tools used and models for management Current and future developments. The second edition evolves from the first by providing additional emphasis and material for those students wishing to specialize in environmental modelling. This edition: Focuses on simplifying complex environmental systems. Reviews current software, tools and techniques for modelling. Gives practical examples from a wide variety of disciplines, e.g. climatology, ecology, hydrology, geomorphology and engineering. Has an associated website containing colour images, links to WWW resources and chapter support pages, including data sets relating to case studies, exercises and model animations. This book is suitable for final year undergraduates and postgraduates in environmental modelling, environmental science, civil engineering and biology who will already be familiar with the subject and are moving on to specialize in the field. It is also designed to appeal to professionals interested in the environmental sciences, including environmental consultants, government employees, civil engineers, geographers, ecologists, meteorologists, and geochemists.
Uncertainty in the predictions of science when applied to the environment is an issue of great current relevance in relation to the impacts of climate change, protecting against natural and man-made disasters, pollutant transport and sustainable resource management. However, it is often ignored both by scientists and decision makers, or interpreted as a conflict or disagreement between scientists. This is not necessarily the case, the scientists might well agree, but their predictions would still be uncertain and knowledge of that uncertainty might be important in decision making. Environmental Modelling: An Uncertain Future? introduces students, scientists and decision makers to: the different concepts and techniques of uncertainty estimation in environmental prediction the philosophical background to different concepts of uncertainty the constraint of uncertainties by the collection of observations and data assimilation in real-time forecasting techniques for decision making under uncertainty. This book will be relevant to environmental modellers, practitioners and decision makers in hydrology, hydraulics, ecology, meteorology and oceanography, geomorphology, geochemistry, soil science, pollutant transport and climate change. A companion website for the book can be found at www.uncertain-future.org.uk
Sensitivity analysis should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. This book guides the non-expert through their problem in order to enable them to choose and apply the most appropriate method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It is focussed on the use of SIMLAB – a widely distributed freely-available sensitivity analysis software package developed by the authors – for solving problems in sensitivity analysis of statistical models. Other key features: Provides an accessible overview of the current most widely used methods for sensitivity analysis. Opens with a detailed worked example to explain the motivation behind the book. Includes a range of examples to help illustrate the concepts discussed. Focuses on implementation of the methods in the software SIMLAB - a freely-available sensitivity analysis software package developed by the authors. Contains a large number of references to sources for further reading. Authored by the leading authorities on sensitivity analysis.