Environmental modelling: an uncertain future? ; an introduction to techniques for uncertainty estimation in environmental prediction
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
London [u.a.]
Routledge
2009
|
| Edition: | 1. publ. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XVII, 310 S. Ill., graph. Darst. |
| ISBN: | 9780415463027 9780415457590 0415463025 0415457599 020393248X |
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| 245 | 1 | 0 | |a Environmental modelling |b an uncertain future? ; an introduction to techniques for uncertainty estimation in environmental prediction |c Keith Beven |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a London [u.a.] |b Routledge |c 2009 | |
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Record in the Search Index
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|---|---|
| adam_text | Contents
List of figures x
List of boxes
xiii
Preface
xiv
1
How to make predictions
1
1.1
The purpose of this book
1
1.2
The aims of environmental modelling
2
1
.3
Seven reasons not to use uncertainty analysis
4
1.4
The nature of the modelling process
5
1.4.1
From perceptual to procedural models
5
1.4.2
Parameters, variables and boundary conditions
7
1.5
The scale problem and the concept of incommensurability
9
1.6
The model space
11
1.7
Ensembles of models
15
1.8
Modelling for formulating understanding
17
1.9
Modelling for practical applications
18
1.9.1
Simulation with no historical data available
18
1.9.2
Simulation with historical data available
20
1.9.3
Forecasting the near future
21
1.10
Guidelines for effective modelling
22
1.11
The meanings of uncertainty
23
1.12
Deciding on an uncertainty estimation method
27
1.13
Uncertainty in model predictions and decision making
27
1.14
Summary of Chapter
1 29
2
A philosophical diversion
31
2.1
Why worry about philosophy?
31
2.2
Pragmatic realism
33
2.3
Other philosophical concepts of realism
35
2.4
Models as instrumentalist tools
35
2.5
The model validation issue
36
vi
Contents
2.6
The model
falsification
issue
38
2.7
The model confirmation issue: Bayesian approaches
39
2.8
The information content of observations as evidence
for the confirmation of models
40
2.9
Explanatory depth and expecting the unexpected
43
2.10
Uncertainty, ignorance, and factors of safety
46
2.11
Summary of Chapter
2 48
3
Simulation with no historical data available
49
3.1
Sensitivity, scenarios and forward uncertainty analysis
49
3.2
Making decisions about prior information
52
3.2.1
Prior distributions of parameters
52
3.2.2
Belief networks
54
3.3
Sampling the model space
56
3.3.1
Analytical propagation of probabilistic uncertainty
57
3.3.2
Discrete samples or random Monte Carlo search?
58
3.3.3
Pseudo-random numbers and the realisation effect
62
3.3.4
Guided Monte Carlo search
65
3.3.5
Copula sampling
67
3.3.6
Case study: Copula sampling in mapping groundwater
quality
70
3.4
Fuzzy representations of uncertainty
70
3.4.1
Case study: Forward uncertainty analysis using fuzzy
variables
73
3.5
Sensitivity analysis
75
3.5.1
Point sensitivity analysis
75
3.5.2
Global sensitivity analysis:
Soboľ
generalised sensitivity
analysis
76
3.5.3
Case study: Application of
Sobol
GSA to a
hydrologie
model
77
3.5.4
Global sensitivity analysis: HSY generalised sensitivity
analysis
77
3.6
Model emulation techniques
81
3.7
Uncertain scenarios
82
3.8
Summary of Chapter
3 83
4
Simulation with historical data available
105
4.1
Model calibration and model conditioning
105
4.2
Weighted nonlinear regression approaches to model
calibration
107
4.2.1
Choosing the cost (objective) function
109
4.2.2
Evaluating parameter and prediction uncertainties
110
Contents
vii
4.2.3
Assessing the value of additional data 111
4.3
Formal Bayesian approaches to model conditioning 111
4.3.1
Formal likelihood measures
113
4.3.2
Markov Chain Monte Carlo search
(MC2) 115
4.3.3
Case study: Assessing uncertainties in a conceptual water
balance model
(Engeland et
al.,
2005) 116
4.4
Pareto optimal sets
117
4.5
Generalised Likelihood Uncertainty Estimation
120
4.5.1
The basis of the GLUE methodology
121
4.5.2
Deciding on whether a model is behavioural or not
123
4.5.3
Equifinality, confidence limits, tolerance limits and
prediction limits
129
4.5.4
Equifinality and model validation
131
4.5.5
Equifinality and model spaces: sampling efficiency issues
133
4.5.6
Fuzzy measures in model evaluation
134
4.5.7
Case study: Hypothesis testing models of stream runoff
generation using GLUE
135
4.5.8
Variants on the GLUE methodology
1 38
4.5.9
What to do if you find that all your models can be
rejected?
140
4.6
Fuzzy systems: conditioning fuzzy rules using data
142
4.7
Comparing methods for model conditioning:
coherence and the information content of data
143
4.8
Summary of Chapter
4 145
Forecasting the near future
171
5.1
Real-time data assimilation
171
5.2
Least squares error correction models
176
5.3
The
Kalman
filter
178
5.3.1
Updating a model of the residual errors
179
5.3.2
Updating the gain on a forecasting model
180
5.3.3
Case study: Flood forecasting on the River Severn
1 8 1
5.3.4
The Extended
Kalman
filter
183
5.4
The Ensemble
Kalman
filter
185
5.4.1
Case study: Application of the Ensemble
Kalman
filter to
the Leaf River Basin
186
5.4.2
The Ensemble
Kalman
smoother
187
5.5
The Particle filter
188
5.5.1
Case study: Comparison of EnKF and PF methods on the
River Rhine
190
5.6
Variational methods
191
5.7
Ensemble methods in weather forecasting
194
5.8
Summary of Chapter
5 194
viii Contents
6
Decision making when faced with uncertainty
207
6.1
Uncertainty and risk in decision making
207
6.2
Uncertainty in framing the decision context
208
6.3
Decision trees, influence diagrams and belief networks
209
6.4
Methods of risk assessment in decision making
211
6.5
Risk-based decision-making methodologies
212
6.5.1
Assessing the preferences of the decision maker
213
6.5.2
Indifference between actions
214
6.5.3
Adding uncertainty and more information
214
6.5.4
Case studies: Decisions for flood warning and control in
Lake
Como,
Italy and the Red River,
N.
Dakota
215
6.6
The use of expert opinion in decision making
216
6.7
Combining the opinions of experts: Bayesian Belief
Networks
217
6.7.1
Adding empirical evidence to a belief network
218
6.7.2
A case study
219
6.8
Evidential Reasoning methods
221
6.8.1
Case Study: Use of Evidential Reasoning in assessing
management options for
Rupa
Tal
Lake Nepal
222
6.9
Decision support systems
223
6.10
Info-Gap decision theory
225
6.10.1
Case study: Info-Gap decision making in designing flood
defences
228
6.11
The issue of ownership of uncertainty in decision making
233
6.12
The NUSAP methodology
237
6.13
Robust adaptive management in the face of uncertainty
238
6.14
Uncertainty and the precautionary principle in
decision making
240
6.15
Summary of Chapter
6 241
7
An uncertain future?
251
7.1
So what should the practitioner do in the face of so
many uncertainty estimation methods?
251
7.2
The problem of future histories
-
unknowability
and uncertainty
254
7.3
But is the uncertainty problem simply a result of
using poor models?
255
7.4
Accepting an uncertain future
256
7.4.1
Modelling as a learning process about places
258
7.4.2
Learning about model structures
258
7.5
Future-proofing modelling systems: adaptive
modelling, adaptive management
260
7.6
Summary of Chapter
7 261
Contents ix
Appendix
I: A (brief) guide to matrix algebra
262
Appendix II: A (brief) guide to software
266
Glossary
269
Bibliography
280
Index
305
|
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| indexdate | 2024-07-09T21:37:10Z |
| institution | BVB |
| isbn | 9780415463027 9780415457590 0415463025 0415457599 020393248X |
| language | English |
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| spelling | Beven, Keith J. Verfasser aut Environmental modelling an uncertain future? ; an introduction to techniques for uncertainty estimation in environmental prediction Keith Beven 1. publ. London [u.a.] Routledge 2009 XVII, 310 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Mathematisches Modell Environmental sciences Mathematical models Umweltwissenschaften (DE-588)4137364-9 gnd rswk-swf Modellierung (DE-588)4170297-9 gnd rswk-swf Modellierung (DE-588)4170297-9 s Umweltwissenschaften (DE-588)4137364-9 s DE-604 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017446480&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Beven, Keith J. Environmental modelling an uncertain future? ; an introduction to techniques for uncertainty estimation in environmental prediction Mathematisches Modell Environmental sciences Mathematical models Umweltwissenschaften (DE-588)4137364-9 gnd Modellierung (DE-588)4170297-9 gnd |
| subject_GND | (DE-588)4137364-9 (DE-588)4170297-9 |
| title | Environmental modelling an uncertain future? ; an introduction to techniques for uncertainty estimation in environmental prediction |
| title_auth | Environmental modelling an uncertain future? ; an introduction to techniques for uncertainty estimation in environmental prediction |
| title_exact_search | Environmental modelling an uncertain future? ; an introduction to techniques for uncertainty estimation in environmental prediction |
| title_full | Environmental modelling an uncertain future? ; an introduction to techniques for uncertainty estimation in environmental prediction Keith Beven |
| title_fullStr | Environmental modelling an uncertain future? ; an introduction to techniques for uncertainty estimation in environmental prediction Keith Beven |
| title_full_unstemmed | Environmental modelling an uncertain future? ; an introduction to techniques for uncertainty estimation in environmental prediction Keith Beven |
| title_short | Environmental modelling |
| title_sort | environmental modelling an uncertain future an introduction to techniques for uncertainty estimation in environmental prediction |
| title_sub | an uncertain future? ; an introduction to techniques for uncertainty estimation in environmental prediction |
| topic | Mathematisches Modell Environmental sciences Mathematical models Umweltwissenschaften (DE-588)4137364-9 gnd Modellierung (DE-588)4170297-9 gnd |
| topic_facet | Mathematisches Modell Environmental sciences Mathematical models Umweltwissenschaften Modellierung |
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