Environmental modelling : an uncertain future?:an introduction to techniques for uncertainty estimation in environmental prediction

Cover image for Environmental modelling : an uncertain future?:an introduction to techniques for uncertainty estimation in environmental prediction

Title:

Personal Author:

Beven, Keith

Publication Information:

London, UK : Routledge, 2009

Physical Description:

xvii, 310 p. : ill. : 26 cm.

ISBN:

9780415463027

Subject Term:

Environmental sciences -- Mathematical models

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Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... FAB Library	32070000000156	GE45.M37 B48 2009	Open Access Book	Book	Searching... Unknown
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Searching... Razak School	30000010297835	GE45.M37 B48 2009	Open Access Book	Book	Searching... Unknown

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

Author Notes

Keith Beven is Professor of Hydrology and Fluid Dynamics at Lancaster University. While finishing this book he was at Uppsala University in Sweden as Konung Carl XVI Gustafs Gästprofessor i Miljövetenskap 2006/07.

List of figures	p. x
List of boxes	p. xiii
Preface	p. xiv
1 How to make predictions	p. 1
1.1 The purpose of this book	p. 1
1.2 The aims of environmental modelling	p. 2
1.3 Seven reasons not to use uncertainty analysis	p. 4
1.4 The nature of the modelling process	p. 5
1.4.1 From perceptual to procedural models	p. 5
1.4.2 Parameters, variables and boundary conditions	p. 7
1.5 The scale problem and the concept of incommensurability	p. 9
1.6 The model space	p. 11
1.7 Ensembles of models	p. 15
1.8 Modelling for formulating understanding	p. 17
1.9 Modelling for practical applications	p. 18
1.9.1 Simulation with no historical data available	p. 18
1.9.2 Simulation with historical data available	p. 20
1.9.3 Forecasting the near future	p. 21
1.10 Guidelines for effective modelling	p. 22
1.11 The meanings of uncertainty	p. 23
1.12 Deciding on an uncertainty estimation method	p. 27
1.13 Uncertainty in model predictions and decision making	p. 27
1.14 Summary of Chapter 1	p. 29
2 A philosophical diversion	p. 31
2.1 Why worry about philosophy?	p. 31
2.2 Pragmatic realism	p. 33
2.3 Other philosophical concepts of realism	p. 35
2.4 Models as instrumentalist tools	p. 35
2.5 The model validation issue	p. 36
2.6 The model falsification issue	p. 38
2.7 The model confirmation issue: Bayesian approaches	p. 39
2.8 The information content of observations as evidence for the confirmation of models	p. 40
2.9 Explanatory depth and expecting the unexpected	p. 43
2.10 Uncertainty, ignorance, and factors of safety	p. 46
2.11 Summary of Chapter 2	p. 48
3 Simulation with no historical data available	p. 49
3.1 Sensitivity, scenarios and forward uncertainty analysis	p. 49
3.2 Making decisions about prior information	p. 52
3.2.1 Prior distributions of parameters	p. 52
3.2.2 Belief networks	p. 54
3.3 Sampling the model space	p. 56
3.3.1 Analytical propagation of probabilistic uncertainty	p. 57
3.3.2 Discrete samples or random Monte Carlo search?	p. 58
3.3.3 Pseudo-random numbers and the realisation effect	p. 62
3.3.4 Guided Monte Carlo search	p. 65
3.3.5 Copula sampling	p. 67
3.3.6 Case study: Copula sampling in mapping groundwater quality	p. 70
3.4 Fuzzy representations of uncertainty	p. 70
3.4.1 Case study: Forward uncertainty analysis using fuzzy variables	p. 73
3.5 Sensitivity analysis	p. 75
3.5.1 Point sensitivity analysis	p. 75
3.5.2 Global sensitivity analysis: Sobol' generalised sensitivity analysis	p. 76
3.5.3 Case study: Application of Sobol' GSA to a hydrologic model	p. 77
3.5.4 Global sensitivity analysis: HSY generalised sensitivity analysis	p. 77
3.6 Model emulation techniques	p. 81
3.7 Uncertain scenarios	p. 82
3.8 Summary of Chapter 3	p. 83
4 Simulation with historical data available	p. 105
4.1 Model calibration and model conditioning	p. 105
4.2 Weighted nonlinear regression approaches to model calibration	p. 107
4.2.1 Choosing the cost (objective) function	p. 109
4.2.2 Evaluating parameter and prediction uncertainties	p. 110
4.2.3 Assessing the value of additional data	p. 111
4.3 Formal Bayesian approaches to model conditioning	p. 111
4.3.1 Formal likelihood measures	p. 113
4.3.2 Markov Chain Monte Carlo search (MC[superscript 2])	p. 115
4.3.3 Case study: Assessing uncertainties in a conceptual water balance model (Engeland et al., 2005)	p. 116
4.4 Pareto optimal sets	p. 117
4.5 Generalised Likelihood Uncertainty Estimation	p. 120
4.5.1 The basis of the GLUE methodology	p. 121
4.5.2 Deciding on whether a model is behavioural or not	p. 123
4.5.3 Equifinality, confidence limits, tolerance limits and prediction limits	p. 129
4.5.4 Equifinality and model validation	p. 131
4.5.5 Equifinality and model spaces: sampling efficiency issues	p. 133
4.5.6 Fuzzy measures in model evaluation	p. 134
4.5.7 Case study: Hypothesis testing models of stream runoff generation using GLUE	p. 135
4.5.8 Variants on the GLUE methodology	p. 138
4.5.9 What to do if you find that all your models can be rejected?	p. 140
4.6 Fuzzy systems: conditioning fuzzy rules using data	p. 142
4.7 Comparing methods for model conditioning: coherence and the information content of data	p. 143
4.8 Summary of Chapter 4	p. 145
5 Forecasting the near future	p. 171
5.1 Real-time data assimilation	p. 171
5.2 Least squares error correction models	p. 176
5.3 The Kalman filter	p. 178
5.3.1 Updating a model of the residual errors	p. 179
5.3.2 Updating the gain on a forecasting model	p. 180
5.3.3 Case study: Flood forecasting on the River Severn	p. 181
5.3.4 The Extended Kalman filter	p. 183
5.4 The Ensemble Kalman filter	p. 185
5.4.1 Case study: Application of the Ensemble Kalman filter to the Leaf River Basin	p. 186
5.4.2 The Ensemble Kalman smoother	p. 187
5.5 The Particle filter	p. 188
5.5.1 Case study: Comparison of EnKF and PF methods on the River Rhine	p. 190
5.6 Variational methods	p. 191
5.7 Ensemble methods in weather forecasting	p. 194
5.8 Summary of Chapter 5	p. 194
6 Decision making when faced with uncertainty	p. 207
6.1 Uncertainty and risk in decision making	p. 207
6.2 Uncertainty in framing the decision context	p. 208
6.3 Decision trees, influence diagrams and belief networks	p. 209
6.4 Methods of risk assessment in decision making	p. 211
6.5 Risk-based decision-making methodologies	p. 212
6.5.1 Assessing the preferences of the decision maker	p. 213
6.5.2 Indifference between actions	p. 214
6.5.3 Adding uncertainty and more information	p. 214
6.5.4 Case studies: Decisions for flood warning and control in Lake Como, Italy and the Red River, N. Dakota	p. 215
6.6 The use of expert opinion in decision making	p. 216
6.7 Combining the opinions of experts: Bayesian Belief Networks	p. 217
6.7.1 Adding empirical evidence to a belief network	p. 218
6.7.2 A case study	p. 219
6.8 Evidential Reasoning methods	p. 221
6.8.1 Case Study: Use of Evidential Reasoning in assessing management options for Rupa Tal Lake Nepal	p. 222
6.9 Decision support systems	p. 223
6.10 Info-Gap decision theory	p. 225
6.10.1 Case study: Info-Gap decision making in designing flood defences	p. 228
6.11 The issue of ownership of uncertainty in decision making	p. 233
6.12 The NUSAP methodology	p. 237
6.13 Robust adaptive management in the face of uncertainty	p. 238
6.14 Uncertainty and the precautionary principle in decision making	p. 240
6.15 Summary of Chapter 6	p. 241
7 An uncertain future?	p. 251
7.1 So what should the practitioner do in the face of so many uncertainty estimation methods?	p. 251
7.2 The problem of future histories - unknowability and uncertainty	p. 254
7.3 But is the uncertainty problem simply a result of using poor models?	p. 255
7.4 Accepting an uncertain future	p. 256
7.4.1 Modelling as a learning process about places	p. 258
7.4.2 Learning about model structures	p. 258
7.5 Future-proofing modelling systems: adaptive modelling, adaptive management	p. 260
7.6 Summary of Chapter 7	p. 261
Appendix I A (brief) guide to matrix algebra	p. 262
Appendix II A (brief) guide to software	p. 266
Glossary	p. 269
Bibliography	p. 280
Index	p. 305

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Table of Contents