Computer-based environmental management

Foreword v Acknowledgments vii Introduction xvii Part I Setting the Scene: Diversity of Environmental Modeling 1 From Conceptual Modeling to Computer Simulations 1 1.1 Introduction 1 1.2 The Modeling Process 3 1.2.1 System Analysis: Conceptual Models 3 1.2.2 Properties: Granularity, Extent and Scale 7 1.2.3 Toolbox and Language: Mathematical Models 10 1.2.4 Results: Computer Models 12 1.3 Model Analysis 14 1.3.1 Verification, Validation and Calibration 14 1.3.2 Intrinsic Verification and Predictive Power 15 1.3.3 Uncertainty 17 1.3.4 Categories and Classifications 18 ix x CONTENTS 1.4 Linking Real World Data and Models 21 1.4.1 Regionalization: Applications to Investigation Sites and Spatial Validity 21 1.4.2 Parameter Estimation 23 1.5 Modeling Languages and Development Platforms 24 1.5.1 Overview 24 1.5.2 Mathematical Languages 25 1.5.3 Generic Tools for Model Development 27 1.5.4 Conceptual Modeling Tools 28 1.5.5 Modeling and Programming Environments 29 1.5.6 Numerical Mathematics 30 1.6 Summary 33 2 Environmental Models: Dynamic Processes 35 2.1 Introduction 35 2.2 First Trophic Level: Primary Producers 35 2.2.1 Crop Growth 36 2.2.2 Temporal Patterns of Annual Plants 37 2.2.3 Nitrogen Uptake 38 2.2.4 Interspecific Competition: Weeds and Weed Control 39 2.3 Parameter Estimation (Part I) 39 2.3.1 Experimental Design of Field Experiments 40 2.3.2 Application of Algorithms 41 2.3.3 Parameters of Crop Growth 43 2.3.4 Competition Models 46 2.3.5 Results 49 2.4 Abiotic Environment: Water and Matter Dynamics 50 2.4.1 Nutrient Cycle: Detritus 51 2.4.2 Xenobiotica Fate: Agrochemicals 52 2.5 Parameter Estimation (Part II) 53 2.5.1 Laboratory Experiments 54 2.5.2 Results 54 2.6 Higher Trophic Levels: Consumers or Pest Infestation 55 2.6.1 Continuous Population Dynamics 55 2.6.2 Age-structured Populations 57 2.6.3 Types of Population Dynamic Models 60 2.7 Model Integration: Generic Agroecosystem Model 62 2.8 Summary 65 CONTENTS xi 3 Environmental Models: Spatial Interactions 67 3.1 Spatial References in Environmental Models 67 3.1.1 Spatial Scales and Model Support 67 3.1.2 Models for Spatial Data Structures 70 3.1.3 Spatial Patterns 72 3.2 Aggregated Spatially Explicit Models 73 3.2.1 Abiotic Processes 73 3.2.2 Biotic Processes 76 3.3 Integrating Spatially Explicit Models 85 3.3.1 Regionalization of Site Models 85 3.3.2 Cellular Automata 89 3.3.3 Generic Landscape Models 91 3.4 Discussion 94 Part II Integrated Models 4 Multi-paradigm Modeling 99 4.1 Introduction 99 4.2 Fundamental Aspects of Environmental Modeling 100 4.3 Mathematics of Environmental Modeling 102 4.3.1 General Model Equation 102 4.3.2 Integrated Models 103 4.4 Model Documentation and Model Databases 104 4.4.1 Introduction 104 4.4.2 Model Databases 105 4.4.3 Meta-modeling Concepts 107 4.5 Summary and Outlook 110 5 Concepts: Hybrid Petri Nets 111 5.1 Introduction 111 5.1.1 Concepts of Hybrid Model Development 111 5.1.2 Aim and Scope of the Development 112 5.2 Theoretical Background 112 5.2.1 Hybrid Low Level Petri Nets 112 5.2.2 Functional Behavior 114 5.3 Development Platform 115 5.3.1 Overview 115 xii CONTENTS 5.3.2 Meta-modeling Concept 117 5.3.3 Core Simulation Algorithm and Model Analysis 117 5.4 An Ecological Modeling Example 118 5.4.1 Predator–Prey Interactions 118 5.4.2 Event-based Modeling of Predator–Prey Interactions 119 5.4.3 Simulation Results 120 5.4.4 Discussion and Extensions 121 5.5 Concluding Remarks 122 6 Case Studies: Hybrid Systems in Ecology 123 6.1 Introduction 123 6.2 Hybrid Crop Growth Models 123 6.2.1 Modeling of Crop Growth with Dynamically Changing Model Structures 123 6.2.2 Hybrid Petri Net 125 6.2.3 Results 126 6.3 The Gal´apagos Archipelago and the Blue-winged Grasshopper 128 6.3.1 Meta-population in Island Biogeography 128 6.3.2 Spatially Explicit Hybrid Petri nets 130 6.3.3 Results 131 6.3.4 Comparison 132 6.4 Summary 135 7 Applications: Environmental Impact Assessment 137 7.1 Introduction 137 7.2 Aim and Scope 138 7.3 Methodology 138 7.3.1 Life Cycle Inventory 139 7.3.2 The Link: Environmental Fate Modeling 140 7.3.3 Fuzzy Expert Systems for Impact Assessment 140 7.4 Life Cycle Inventory of the Production Process 143 7.5 Environmental Fate Modeling of NOx-Emissions 145 7.5.1 Overview 145 7.5.2 Atmospheric Transport Model 146 7.5.3 Process Model 148 7.5.4 Results 150 CONTENTS xiii 7.6 Environmental Impact Assessment 151 7.6.1 Soil Acidification 151 7.6.2 Eutrophication 152 7.6.3 Plant Damage 154 7.7 Discussion 154 Part III The Big Picture: Environmental Management 8 Scenario Analysis and Optimization 159 8.1 Introduction 159 8.2 Optimization and Environmental Modeling 161 8.2.1 Analytical Treatment and Non-spatial Applications 161 8.2.2 Spatially Explicit Applications 162 8.3 Assessing the Environment Variables 162 8.3.1 Indicators 162 8.3.2 . . . and Applications for Optimization 166 8.4 General Optimization Task 167 8.4.1 Performance Criteria 167 8.4.2 General Optimization Task 169 8.4.3 Methodology 170 8.5 Discussion 171 9 Prerequisites: Temporal Hierarchies and Spatial Scales 173 9.1 Introduction 173 9.2 Hierarchical Dynamic Programming 174 9.2.1 Introduction 174 9.2.2 Hierarchies and Temporal Scales 176 9.2.3 Program Library 180 9.2.4 Concluding Remarks 182 9.3 Optimization and Spatially Explicit Models 182 9.3.1 Computational Effort 183 9.3.2 Local and Global Performance Criteria 183 9.3.3 Grid Search Strategy on Local Problem 185 9.3.4 Disturbing a Solution: Monte Carlo Simulation 185 9.3.5 Genetic Algorithm Solving the Global Problem 187 9.3.6 Toolbox for Spatially Explicit Optimization 188 xiv CONTENTS 9.4 Summary 191 10 Optimum Agroecosystem Management: Temporal Patterns 193 10.1 Introduction 193 10.2 Assessing the State of an Agroecosystem 194 10.2.1 External Cost and Non-measurable Variables 194 10.2.2 Performance Criteria 194 10.2.3 Weighting Schemes 195 10.3 Agricultural Optimum Control Problem 196 10.3.1 Optimization Task 196 10.3.2 Hierarchical Structure of the Problem 197 10.4 Short-term Solutions: Managing a Vegetation Period 198 10.4.1 Optimum Fertilizing Schemes 198 10.4.2 Optimum Pesticide Application Timing 199 10.5 Long-term Solutions: Managing Crop Rotations 201 10.5.1 Nutrient Balance 201 10.5.2 Pest Control 201 10.6 Discussion 202 11 Optimum Agroecosystem Management: Spatial Patterns 207 11.1 Introduction 207 11.1.1 Site-specific Agroecological Modeling 207 11.1.2 Aims, Scope and Region 208 11.2 Optimum Control in Regionalized Models 208 11.2.1 Agroecological Simulation Model 208 11.2.2 Optimization Task 210 11.3 Concept of Optimum Spatial Control 210 11.4 Optimization and Simulation Experiments 213 11.4.1 Types of Spatial Solutions 213 11.4.2 Results 216 11.5 Discussion 217 12 Changing Landscapes: Optimum Landscape Patterns 221 12.1 Introduction 221 12.2 Performance Criteria for Landscape Optimization 223 12.2.1 Economic–Ecologic Assessment 223 12.2.2 Localization of Optimization Problem 225 12.2.3 Multi-criteria Assessment of Ecosystem Functions 226 CONTENTS xv 12.2.4 Numerical Effort 227 12.3 Validation of Concept: Results for Hunting Creek Watershed 228 12.3.1 Local Optimization 228 12.3.2 Monte Carlo Simulations 229 12.3.3 Statistical Analysis 232 12.3.4 Genetic Algorithms 233 12.4 Results of Multi-criteria Optimization 235 12.4.1 General Results for Optimum Land Use Patterns 235 12.4.2 Scenarios of Optimized Land Use Patterns 239 12.5 Climatic Variability and Optimum Land Use Patterns 244 12.6 Multi-scale Analysis of Landscape Patterns 244 12.6.1 Distance Measure of Discrete Maps 246 12.6.2 “Correlation”-analysis of Landscape Patterns 247 12.6.3 Optimization Results on Differing Scales 248 12.7 Summary and Outlook 250 12.7.1 Methodological Aspects 250 12.7.2 Optimization Results as Multi-stage Decision Process 251 12.7.3 Application of Results 251 12.7.4 Patterns and Processes 252 12.7.5 Outlook 253 13 Conclusions, Perspectives and Research Demands 255 13.1 Retrospection 255 13.2 Conclusions 256 13.3 Perspectives 257 References 259 Additional References 279 Web Ressources 279 Copyrights and Sources 279 Quotations 280 Index 281