Environmental foresight and models : a manifesto /

Table Of Contents:
Preface xiii
PART I. THE MANIFESTO

Introduction 3(8)

M.B. Beck

An Old Familiar Problem 3(4)

Embarking on a Change of Perspective 7(4)

References 9(2)

We Have A Problem 11(24)

M.B. Beck

Limits to the Science Base 11(5)

Ripples Across the Community of Environmental Scientists 16(1)

Lake Erie and Eutrophication: Youthful Exuberance of Systems Ecology 17(3)

Surface Water Acidification: Insufficiency of a Physics-based Programme 20(4)

Tropospheric Ozone Control: Dynamics of Science and Policy out of Phase 24(2)

A Global Picture: Stabilising Greenhouse Gas Concentrations (and Stabilising Our Forecasts) 26(2)

Science Base Changing Under Our Feet 28(7)

References 30(5)

Beginnings of a Change of Perspective 35(16)

M.B. Beck

State Variables Masquerading as Parameters 35(6)

Reconciling Theory with Observation 41(4)

Erasing the Divide Between Past and Future 45(2)

A Knot of Constraints, Opportunities and Contradictions 47(4)

References 48(3)

Structural Change: A Definition 51(10)

M.B. Beck

A Metaphor and Some Accompanying Conceptual Apparatus 51(6)

An Example 57(1)

The Nub of the Problem 58(3)

References 60(1)

The Manifesto 61(34)

M.B. Beck

The Challenge 61(1)

Belief Networks: Generating the Feared Dislocations 62(3)

High-Order Models: Random Search and the Reachability of Target Futures 65(5)

Evolving Clusters of Candidate Parameterisations 70(2)

Simplicity out of Complexity 72(3)

Parameterising Parametric Change 75(2)

Elasto-Plastic Deformation of the Structure 77(4)

Detecting and Forecasting Growth in the Seeds of Structural Change 81(4)

Probing the Shores of Ignorance 85(4)

Visualisation and Learning 89(1)

Foresight for Action 90(5)

References 91(4)

Epilogue 95(10)

M.B. Beck

An Evolutionary Approach in Form 95(2)

Parametric Change as the Agent of Control 97(2)

Inclined to Survive (or Otherwise) 99(6)

References 102(3)
PART II. CASE HISTORIES

Lake Erie and Evolving Issues of the Quality of its Water 105(26)

W.M. Schertzer

D.C.L. Lam

Introduction 105(1)

Eutrophication 106(14)

Emerging Perception of a Problem 106(1)

Delayed Response: A Public Call to Arms 107(1)

Understanding the System: the {Known} versus Our {Ignorance} 107(2)

Synthesis: A Low-Order Model (LOM) 109(1)

Formulating Policy: Great Lakes Water Quality Agreement (GLWQA) 110(1)

And the Auditing of Policy 111(1)

Back to the Bench 111(1)

Towards Higher-Order Models (HOMs) 112(2)

The Keys to Insight and Understanding 114(6)

``Unforeseen'' Stresses of the Late 1980s and 1990s 120(3)

Looking Beyond the Horizon: Climate Change 120(3)

Exotic Species 123(1)

Lessons Learned 123(8)

Acknowledgements 126(1)

References 126(5)

Impacts of Acidic Atmospheric Deposition on the Chemical Composition of Stream Water and Soil Water 131(16)

G.M. Hornberger

Introduction 131(1)

Background 132(2)

Direct Use of Data 133(1)

Conceptual Nature of Acidification Models 133(1)

The Case of Magic: A Tool for Long-Term Forecasting 134(7)

Application to Regional Assessments 136(4)

Uncertainty in Model Structure 140(1)

Status and Future Directions 141(6)

References 143(4)

The Ozone Problem 147(22)

R.L. Dennis

Background and Context 147(2)

The Essential Problem 147(1)

The Challenge for Management 148(1)

The Nature of Management Cycles 149(1)

Evolution of Models of Air Quality 149(3)

Representation of Chemistry 150(1)

Representation of Transformation Coupled with Transport 151(1)

The Forecasting Problem: The Importance of Isoprene Emissions 152(1)

Forecasting Impacts in Perspective 153(7)

Retrospective Results for Spatially Uniform, Widespread Reductions 154(2)

Retrospective Results for Urban-focused NOx Reductions 156(2)

In Summary 158(2)

Difficulty Discerning the Truth 160(1)

The Jerky Exchange Between Policy and Science 161(1)

Lessons to be Learned 162(7)

References 164(5)
PART III. THE APPROACH

Belief Networks: Generating the Feared Dislocations 169(38)

O. Varis

On Certainties and Uncertainties 169(2)

Are We Overconfident in Our Conventional Numerical Models? 171(3)

Impacts of Climate Change on Finnish Watersheds 174(12)

Methodology: Belief Networks 177(4)

Interviews: Collection of the Data 181(1)

Sample Results: Scientists are in Private Much More Uncertain than Their Scientific Papers Would Suggest 182(4)

Back to the Global Scale 186(9)

Interdisciplinary Analysis on a Global Scale: A Melange of Vicious Circles 189(6)

Epilogue and Extensions 195(12)

References 196(3)

Appendix: Computational Solution of Belief Networks 199(8)

Random Search and the Reachability of Target Futures 207(20)

M.B. Beck

J. Chen

O.O. Osidele

Introduction 207(1)

Reachable Futures 208(3)

A Regionalised Sensitivity Analysis 211(6)

Origins of the Method 211(1)

Essence of the Analysis 212(3)

Target Futures: Beliefs, Imagination, and Experience as Substitutes for Quantitative Observation 215(2)

A Case Study: Aquatic Foodweb in a Piedmont Impoundment 217(7)

Structure of the Model 217(2)

Matching the Past and Reaching Future Behaviour 219(3)

Taking Stock 222(2)

Conclusions 224(3)

References 225(2)

Uncertainty and the Detection of Structural Change in Models of Environmental Systems 227(24)

K.J. Beven

Uncertainty and Change in Modelling Environmental Systems 227(1)

An Example 228(2)

Equifinality and Uncertainty in Environmental Models 230(2)

Equifinality and Change in Environmental Systems 232(1)

The GLUE Methodology: Rationale 233(6)

Requirements of GLUE 235(1)

Results of Using Different Likelihood Measures 236(1)

Calculation of Likelihood Distributions and Uncertainty Bounds 237(1)

Updating of Uncertainty Bounds 238(1)

On the Sensitivity to Individual Parameters 239(1)

Uncertainty and Predicting the Effects of Change 239(6)

Conclusions 245(6)

Acknowledgements 246(1)

References 247(4)

Simplicity Out of Complexity 251(52)

P.C. Young

S. Parkinson

Introduction 251(3)

A General Data-based Mechanistic (DBM) Approach to Modelling Complex Environmental Systems 254(4)

Methodological Basis of the DBM Approach 258(7)

The Stochastic Simulation Model and Regionalised Sensitivity Analysis (RSA) 258(3)

Linearisation and Order Reduction of the Non-linear Simulation Models 261(3)

DBM Modelling from Real Data 264(1)

Case Study: The Enting-Lassey Global Carbon Cycle Model 265(23)

Initial Stochastic Model Formulation and Simulation 266(3)

Regionalised Sensitivity Analysis (RSA) 269(1)

Stochastic Model Optimisation 270(2)

Uncertainty in Model Predicted Future Levels of Atmospheric Carbon Dioxide 272(2)

Dominant Mode Analysis (DMA): Model Linearisation and Order Reduction 274(6)

Simple Mechanistic Interpretation of the Reduced-Order Model Results 280(1)

Another Example of Model Reduction 281(1)

Comments 281(1)

Modelling From Real Data 282(6)

Conclusions 288(15)

Acknowledgement 290(1)

References 290(5)

Appendix 1: SRIV Identification and Estimation 295(4)

Appendix 2: The Enting-Lassey Global Carbon Cycle Model 299(4)

Structural Effects of Landscape and Land Use on Streamflow Response 303(20)

T.S. Kokkonen

A.J. Jakeman

Introduction 303(2)

The Model and Some Preliminaries 305(6)

Model 306(2)

Assessment Criteria 308(1)

Removing Climate Dependency 308(3)

Case Study 1: Coweeta, USA 311(6)

Case Study 2: Yass River 317(3)

Conclusions 320(3)

References 320(3)

Elasto-plastic Deformation of Structure 323(28)

M.B. Beck

J.D. Stigter

D. Lloyd Smith

Introduction 323(2)

Apparent Structural Change: A Lesser Form of Evolution 324(1)

A Motivating Example 325(4)

Problem Definition 327(2)

A Metaphor 329(6)

The Conceptual Framework of Recursive Prediction 330(2)

Tracing Structural Error in the {Presumed Known} and the {Acknowledged Unknown} 332(3)

Design for Plastic Deformation 335(5)

State Estimation and Model Structure Identification 335(3)

A Recursive Prediction Error Algorithm 338(2)

Some Material Properties of the Gain Matrix 340(1)

Case Studies 340(4)

Structural Error in the {Presumed Known} 341(1)

Structural Error in the {Acknowledged Unknown} 342(2)

Reorienting the Goal Function 344(3)

Bringing Out the Inner Parametric Space of the Model Structure 346(1)

Conclusions 347(4)

References 349(2)

Detecting and Forecasting Growth in the Seeds of Change 351(24)

J. Chen

M.B. Beck

Introduction 351(1)

An Important Variation on the Basic Theme of Structural Change 352(5)

Types of Structural Change 353(2)

Detecting the Seeds of Change 355(2)

Case Study 357(15)

Single-state Model of BOD Dynamics 359(3)

Two-state Model of BOD-DO Dynamics 362(7)

Propagation of the Seeds of Structural Change 369(3)

Conclusions 372(3)

References 373(2)

Probing the Shores of Ignorance 375(40)

R.L. Dennis

J.R. Arnold

G.S. Tonnesen

Introduction 375(3)

HOMs, Explanation, and Prediction 376(2)

Probing Our Ignorance of the Unpacked Parts of the HOM 378(2)

A High-level Conceptual Description 378(1)

Instrumenting the HOM 379(1)

Developing New Diagnostic Measures Using the Conceptual Mental Model and the Instrumented HOM 380(1)

Case Study of Tropospheric Photochemistry 380(8)

Understanding Tropospheric Photochemistry 381(4)

Model Representations 385(1)

Taxonomy of Diagnostic Measures 386(2)

Instrumenting the Model 388(8)

Background 388(2)

Illustrations from RADM 390(6)

Connecting Model Probes to Ambient Field Observations 396(7)

Associating Field Observations with Diagnostic Model Measures 396(3)

What Observations Can We Make at Present? 399(4)

Design for Discovery: Probing a Regional Photochemical Model 403(5)

Indicators of Structural Change: O3 versus NOz under NOx-limited Conditions 403(3)

The Radical Maintenance Pool: Measures of OH Propagation 406(1)

Process-oriented Testing of Component Mechanisms: A Newly Proposed OH+NO2 Reaction-Rate Constant 406(2)

Conclusions 408(7)

References 409(6)
PART IV. EPILOGUE

Parametric Change as the Agent of Control 415(10)

K.J. Keesman

Introduction 415(1)

Feedback 416(4)

Large Time Constants and Low Sampling/control Frequencies 419(1)

Model-based Predictive Control 420(2)

Conclusions: But the Very Beginnings of an Approach 422(3)

References 423(2)

Identifying the Inclination of a System Towards a Terminal State from Current Observations 425(28)

A.V. Kryazhimskii

M.B. Beck

Introduction 425(2)

Binary Models and Problem Definition 427(5)

Hypotheses 427(1)

Designing a Binary Model 428(1)

An Elementary Illustration 429(2)

Problem Definition 431(1)

Identification of Inclination from Current Observations 432(12)

Step 0: Construction of the Binary Model 432(1)

Step 1: Model-based Analysis (No Observations) 432(4)

Step 2: Model and Data-based Analysis 436(1)

Step 3: Comparing the Results From Steps 1 and 2 437(1)

Step 4: Separating the Space of Models 437(1)

Step 5: Posterior Assumptions 438(1)

Step 6: Dominance Analysis 439(5)

Case Study: Rodent Population in the Vicinity of Chernobyl, Ukraine 444(4)

The Method: In Retrospect and Prospect 448(1)

Conclusions 449(4)

Acknowledgements 450(1)

References 450(3)
Contributing Authors - Biosketches 453(8)
Subject Index 461