Computer–Based Environmental Management

The major focus of Dr. Ralf Seppelt's research is the application of mathematical methodologies for solving environmental problems. He entered this interdisciplinary field upon obtaining a diploma in applied mathematics from the Technical University Clausthal, Germany, in 1994 and with his mater's thesis on socio ecological models. He continued as a research associate at the Collaborative Research Center Water and Matter Dynamics of Agroecosystems in Brunswick, where he obtain his doctorate with distinction in science, agroecology, and system analysis in 1997. Over the last few years he has managed several research projects in cooperation with different research institutions as well as industry and with small companies. In 2000 Dr. Seppelt was visiting scientist at the Institute of Ecological Economics, Maryland, USA. Ralf Seppelt has published more than 30 scientific articles and is a reviewer for several international journals. His successful research in the field of environmental science was made possible by his productive interdisciplinary teaching of undergraduate and graduate students. At the TU Brunswick, Dr. Seppelt lectures in computer science, geographic information systems (GIS), theoretical ecological, and numerical environmental modeling.

Foreword.Acknowledgments.Introduction.Part I: Setting the Scene: Diversity of Environmental Modeling.1 From Conceptual Modeling to Computer Simulations.1.1 Introduction.1.2 The Modeling Process.1.2.1 System Analysis: Conceptual Models.1.2.2 Properties: Granularity, Extent and Scale.1.2.3 Toolbox and Language: Mathematical Models.1.2.4 Results: Computer Models.1.3 Model Analysis.1.3.1 Verification, Validation and Calibration.1.3.2 Intrinsic Verification and Predictive Power.1.3.3 Uncertainty.1.3.4 Categories and Classifications.1.4 Linking Real World Data and Models.1.4.1 Regionalization: Applications to Investigation Sites and Spatial Validity.1.4.2 Parameter Estimation.1.5 Modeling Languages and Development Platforms.1.5.1 Overview.1.5.2 Mathematical Languages.1.5.3 Generic Tools for Model Development.1.5.4 Conceptual Modeling Tools.1.5.5 Modeling and Programming Environments.1.5.6 Numerical Mathematics.1.6 Summary.2 Environmental Models: Dynamic Processes.2.1 Introduction.2.2 First Trophic Level: Primary Producers.2.2.1 Crop Growth.2.2.2 Temporal Patterns of Annual Plants.2.2.3 Nitrogen Uptake.2.2.4 Interspecific Competition: Weeds and Weed Control.2.3 Parameter Estimation (Part I).2.3.1 Experimental Design of Field Experiments.2.3.2 Application of Algorithms.2.3.3 Parameters of Crop Growth.2.3.4 Competition Models.2.3.5 Results.2.4 Abiotic Environment: Water and Matter Dynamics.2.4.1 Nutrient Cycle: Detritus.2.4.2 Xenobiotica Fate: Agrochemicals.2.5 Parameter Estimation (Part II).2.5.1 Laboratory Experiments.2.5.2 Results.2.6 Higher Trophic Levels: Consumers or Pest Infestation.2.6.1 Continuous Population Dynamics.2.6.2 Age structured Populations.2.6.3 Types of Population Dynamic Models.2.7 Model Integration: Generic Agroecosystem Model.2.8 Summary.3 Environmental Models: Spatial Interactions.3.1 Spatial References in Environmental Models.3.1.1 Spatial Scales and Model Support.3.1.2 Models for Spatial Data Structures.3.1.3 Spatial Patterns.3.2 Aggregated Spatially Explicit Models.3.2.1 Abiotic Processes.3.2.2 Biotic Processes.3.3 Integrating Spatially Explicit Models.3.3.1 Regionalization of Site Models.3.3.2 Cellular Automata.3.3.3 Generic Landscape Models.3.4 Discussion.Part II: Integrated Models.4 Multi paradigm Modeling.4.1 Introduction.4.2 Fundamental Aspects of Environmental Modeling.4.3 Mathematics of Environmental Modeling.4.3.1 General Model Equation.4.3.2 Integrated Models.4.4 Model Documentation and Model Databases.4.4.1 Introduction.4.4.2 Model Databases.4.4.3 Meta modeling Concepts.4.5 Summary and Outlook.5 Concepts: Hybrid Petri Nets.5.1 Introduction.5.1.1 Concepts of Hybrid Model Development.5.1.2 Aim and Scope of the Development.5.2 Theoretical Background.5.2.1 Hybrid Low Level Petri Nets.5.2.2 Functional Behavior.5.3 Development Platform.5.3.1 Overview.5.3.2 Meta modeling Concept.5.3.3 Core Simulation Algorithm and Model Analysis.5.4 An Ecological Modeling Example.5.4.1 Predator Prey Interactions.5.4.2 Event based Modeling of Predator Prey Interactions.5.4.3 Simulation Results.5.4.4 Discussion and Extensions.5.5 Concluding Remarks.6 Case Studies: Hybrid Systems in Ecology.6.1 Introduction.6.2 Hybrid Crop Growth Models.6.2.1 Modeling of Crop Growth with Dynamically Changing Model Structures.6.2.2 Hybrid Petri Net.6.2.3 Results.6.3 The Galapagos Archipelago and the Blue winged Grasshopper.6.3.1 Meta population in Island Biogeography.6.3.2 Spatially Explicit Hybrid Petri nets.6.3.3 Results.6.3.4 Comparison.6.4 Summary.7 Applications: Environmental Impact Assessment.7.1 Introduction.7.2 Aim and Scope.7.3 Methodology.7.3.1 Life Cycle Inventory.7.3.2 The Link: Environmental Fate Modeling.7.3.3 Fuzzy Expert Systems for Impact Assessment.7.4 Life Cycle Inventory of the Production Process.7.5 Environmental Fate Modeling of NOx Emissions.7.5.1 Overview.7.5.2 Atmospheric Transport Model.7.5.3 Process Model.7.5.4 Results.7.6 Environmental Impact Assessment.7.6.1 Soil Acidification.7.6.2 Eutrophication.7.6.3 Plant Damage.7.7 Discussion.Part III: The Big Picture: Environmental Management.8 Scenario Analysis and Optimization.8.1 Introduction.8.2 Optimization and Environmental Modeling.8.2.1 Analytical Treatment and Non spatial Applications.8.2.2 Spatially Explicit Applications.8.3 Assessing the Environment Variables.8.3.1 Indicators.8.3.2 ... and Applications for Optimization.8.4 General Optimization Task.8.4.1 Performance Criteria.8.4.2 General Optimization Task.8.4.3 Methodology.8.5 Discussion.9 Prerequisites: Temporal Hierarchies and Spatial Scales.9.1 Introduction.9.2 Hierarchical Dynamic Programming.9.2.1 Introduction.9.2.2 Hierarchies and Temporal Scales.9.2.3 Program Library.9.2.4 Concluding Remarks.9.3 Optimization and Spatially Explicit Models.9.3.1 Computational Effort.9.3.2 Local and Global Performance Criteria.9.3.3 Grid Search Strategy on Local Problem.9.3.4 Disturbing a Solution: Monte Carlo Simulation.9.3.5 Genetic Algorithm Solving the Global Problem.9.3.6 Toolbox for Spatially Explicit Optimization.9.4 Summary.10 Optimum Agroecostem Management: Temporal Patterns.10.1 Introduction.10.2 Assessing the State of an Agroecosystem.10.2.1 External Cost and Non measurable Variables.10.2.2 Performance Criteria.10.2.3 Weighting Schemes.10.3 Agricultural Optimum Control Problem.10.3.1 Optimization Task.10.3.2 Hierarchical Structure of the Problem.10.4 Short term Solutions: Managing a Vegetation Period.10.4.1 Optimum Fertilizing Schemes.10.4.2 Optimum Pesticide Application Timing.10.5 Long term Solutions: Managing Crop Rotations.10.5.1 Nutrient Balance.10.5.2 Pest Control.10.6 Discussion.11 Optimum Agroecostem Management: Spatial Patterns.11.1 Introduction.11.1.1 Site specific Agroecological Modeling.11.1.2 Aims, Scope and Region.11.2 Optimum Control in Regionalized Models.11.2.1 Agroecological Simulation Model.11.2.2 Optimization Task.11.3 Concept of Optimum Spatial Control.11.4 Optimization and Simulation Experiments.11.4.1 Types of Spatial Solutions.11.4.2 Results.11.5 Discussion.12 Changing Landscapes: Optimum Landscape Patterns.12.1 Introduction.12.2 Performance Criteria for Landscape Optimization.12.2.1 Economic Ecologic Assessment.12.2.2 Localization of Optimization Problem.12.2.3 Multi criteria Assessment of Ecosystem Functions.12.2.4 Numerical Effort.12.3 Validation of Concept: Results for Hunting Creek Watershed.12.3.1 Local Optimization.12.3.2 Monte Carlo Simulations.12.3.3 Statistical Analysis.12.3.4 Genetic Algorithms.12.4 Results of Multi criteria Optimization.12.4.1 General Results for Optimum Land Use Patterns.12.4.2 Scenarios of Optimized Land Use Patterns.12.5 Climatic Variability and Optimum Land Use Patterns.12.6 Multi scale Analysis of Landscape Patterns.12.6.1 Distance Measure of Discrete Maps.12.6.2 Correlation analysis of Landscape Patterns.12.6.3 Optimization Results on Differing Scales.12.7 Summary and Outlook.12.7.1 Methodological Aspects.12.7.2 Optimization Results as Multi stage Decision Process.12.7.3 Application of Results.12.7.4 Patterns and Processes.12.7.5 Outlook.13 Conclusions, Perspectives and Research Demands.13.1 Retrospection.13.2 Conclusions.13.3 Perspectives.References.Additional References.Web Resources.Copyrights and Sources.Quotations.Index.