Muutke küpsiste eelistusi

Anticipatory Water Management Using ensemble weather forecasts for critical events: UNESCO-IHE Phd Thesis [Pehme köide]

  • Formaat: Paperback / softback, 182 pages, kõrgus x laius: 246x174 mm, kaal: 294 g
  • Ilmumisaeg: 15-Jan-2010
  • Kirjastus: CRC Press
  • ISBN-10: 0415573807
  • ISBN-13: 9780415573801
Teised raamatud teemal:
Anticipatory Water Management Using ensemble weather forecasts for critical events: UNESCO-IHE Phd ThesisSuurem pilt
  • Formaat: Paperback / softback, 182 pages, kõrgus x laius: 246x174 mm, kaal: 294 g
  • Ilmumisaeg: 15-Jan-2010
  • Kirjastus: CRC Press
  • ISBN-10: 0415573807
  • ISBN-13: 9780415573801
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780415573801.html
Märksõnad:

Day-to-day water management is challenged by meteorological extremes, causing floods and droughts. Often operational water managers are informed too late about these upcoming events to be able to respond and mitigate their effects, such as by taking flood control measures or even requiring evacuation of local inhabitants. Therefore, the use of weather forecast information with hydrological models can be invaluable for the operational water manager to expand the forecast horizon and to have time to take appropriate action. This is called Anticipatory Water Management.

Anticipatory actions may have adverse effects, such as when flood control actions turn out to have been unnecessary, because the actual rainfall was less than predicted. Therefore the uncertainty of the forecasts and the associated risks of applying Anticipatory Water Management have to be assessed. To facilitate this assessment, meteorological institutes are providing ensemble predictions to estimate the dynamic uncertainty of weather forecasts. This dissertation presents ways of improving the end-use of ensemble predictions in Anticipatory Water Management.

Foreword v
Nowledgements vii
Summary ix
1 Introduction
17(10)
1.1 Background
17(6)
1.1.1 Hydroinformatics and Integrated Water Resources Management
17(1)
1.1.2 Management of extreme events
18(1)
1.1.3 Operational water management
19(1)
1.1.4 Benefits of increased forecast horizon
20(1)
1.1.5 Use of weather forecasts
21(1)
1.1.6 Ensemble forecasts
22(1)
1.2 Anticipatory Water Management
23(2)
1.3 Hypotheses and Objectives
25(1)
1.4 Reader
26(1)
2 Anticipatory Water Management
27(28)
2.1 Introduction
27(1)
2.2 Operational Water Management
27(7)
2.2.1 Definition
27(1)
2.2.2 Components of operational water management
27(2)
2.2.3 Water system control
29(2)
2.2.4 A Reservoirs and polders
31(1)
2.2.5 Flood early warning and control
32(1)
2.2.6 Challenges in operational water management
33(1)
2.3 Weather Forecasting and Ensemble Predictions
34(7)
2.3.1 Monitoring systems
34(2)
2.3.2 From hand-drawn weather maps to numerical prediction
36(1)
2.3.3 From deterministic to probabilistic forecasts
37(1)
2.3.4 Ensemble Prediction Systems
38(2)
2.3.5 Challenges in using weather forecasts for water management
40(1)
2.4 Modelling Controlled Water Systems
41(5)
2.4.1 Definitions
41(1)
2.4.2 Model components
42(1)
2.4.3 Water system state prediction
43(1)
2.4.4 Challenges in modelling controlled water systems
44(2)
2.5 Decision Making with Uncertainty
46(5)
2.5.1 Uncertainty
46(1)
2.5.2 Risk
47(1)
2.5.3 Threshold-based decision rules for Ensemble Prediction Systems
48(1)
2.5.4 Cost-benefit analysis
49(1)
2.5.5 Decision Support Systems for Anticipatory Water Management
50(1)
2.6 Knowledge Gaps and Hypotheses
51(4)
3 Framework for Developing Anticipatory Water Management (awm)
55(26)
3.1 Introduction
55(1)
3.2 Establishing the need and potential for AWM
55(8)
3.2.1 For which events is AWM needed
55(5)
3.2.2 Potential for anticipatory management action
60(3)
3.3 Verification analysis
63(4)
3.3.1 Product selection: time scales, spatial scales
63(1)
3.3.2 Continuous simulation of the real-time AWM forecasting system
63(2)
3.3.3 Event based verification of a range of decision rules for AWM
65(2)
3.4 Modelling Controlled Water Systems
67(2)
3.4.1 Input data based on end-use of model
68(1)
3.4.2 Framework for modelling controlled water systems
68(1)
3.5 Strategies for Anticipatory water Management
69(4)
3.5.1 Rule-based
70(1)
3.5.2 Pre-processing of ensemble forecasts to deterministic forecast
71(1)
3.5.3 Risk-based
71(2)
3.6 Cost-benefit of Selected AWM Strategies
73(2)
3.6.1 Dynamic cost-benefit analysis
73(1)
3.6.2 Sources of damage
74(1)
3.6.3 Anticipatory Water Management modelling
74(1)
3.7 Optimisation of Anticipatory Water Management
75(3)
3.7.1 Objectives
76(1)
3.7.2 Parameterisation of AWM strategies
76(1)
3.7.3 Optimisation using perfect forecasts
77(1)
3.7.4 Optimisation with actual forecasts
77(1)
3.8 Decision making for policy adoption of AWM
78(1)
3.8.1 What-if analysis
78(1)
3.8.2 Re-analysis era
79(1)
3.9 Framework for Developing Anticipatory Water Management
79(2)
4 Case study 1 - Rijnland Water System
81(46)
4.1 Introduction
81(2)
4.2 Problem Description
83(1)
4.3 Data
84(2)
4.4 Water system control model
86(17)
4.4.1 Model structure
86(2)
4.4.2 Control strategy
88(1)
4.4.3 Model calibration
89(1)
4.4.4 Model validation
90(2)
4.4.5 Visualise what is not known and explain
92(3)
4.4.6 Modelling the unknown phenomena
95(2)
4.4.7 Final model results
97(5)
4.4.8 Discussion
102(1)
4.5 Ensemble Forecasts Verification
103(11)
4.5.1 Precipitation ensemble forecasts archive
103(1)
4.5.2 Water level hindcasts
103(1)
4.5.3 Event based verification for water Managers
104(1)
4.5.4 Precipitation and water level thresholds
105(1)
4.5.5 Presently used precipitation threshold for anticipatory pumping
105(3)
4.5.6 3-Day accumulated precipitation threshold for selected events
108(1)
4.5.7 5-Day accumulated precipitation threshold for selected events
109(2)
4.5.8 Discussion
111(3)
4.6 Anticipatory Water Management Strategy Development
114(2)
4.7 Cost-benefit of Selected AWM Strategies
116(4)
4.7.1 Water level-damage function
116(2)
4.7.2 Inter-comparison of costs for selected strategies
118(2)
4.8 Optimisation of Anticipatory Water Management Strategy
120(5)
4.8.1 Optimisation with perfect forecasts
120(2)
4.8.2 Optimisation with actual forecasts
122(3)
4.9 Adoption of AWM in Operational Management Policy
125(2)
5 Case Study 2 - Upper Blue Nile
127(14)
5.1 Introduction
127(1)
5.2 Problem Description
127(1)
5.3 Data
128(3)
5.3.1 Geographical data
128(1)
5.3.2 Meteorological data
128(2)
5.3.3 Streamflow data
130(1)
5.4 Hydrological Model
131(4)
5.4.1 Model set-up
131(1)
5.4.2 Calibration and validation
132(3)
5.5 Ensemble Forecasts Verification
135(9)
5.5.1 Event selection
135(3)
5.5.2 Ensemble precipitation hindcasts
138(1)
5.5.3 Ensemble streamflow hindcasts
138(1)
5.5.4 Verification analysis
138(1)
5.5.5 Statistical verification
139(1)
5.5.6 Comparison by visual inspection
140(2)
5.5.7 Flood early warning verification
142(2)
5.6 Anticipatory Management Strategy Development
144(1)
5.7 Adoption of AWM in Operational Management Policy
145
6 Conclusions and Recommendations47
141
6.1 Contributions to Anticipatory Water Management
147(2)
6.2 Discussion of the Hypotheses
149(2)
6.3 Conclusions
151(1)
6.4 Recommendations for Management Practice
152(1)
6.5 Recommendations for further research
153(6)
References 159(8)
List of Figure 167(6)
About the author 173(4)
Samenvatting 177
Schalk Jan van Andel (1978) graduated (with distinction) for his MSc degree in Integrated and quantitative water management from Wageningen University (2003). He specialised in the development and application of hydrological and hydrodynamic models. After graduating he worked as a specialist water management with HydroLogic, The Netherlands, and as a project officer with the Netherlands Water Partnership (NWP). In 2004 he joined UNESCO-IHE (Hydroinformatics and Knowledge Management department where he started the PhD research presented in this dissertation. At present Schalk Jan is a lecturer in Hydroinformatics at UNESCO-IHE, Delft, The Netherlands. His research interest concerns the application of meteorological data and forecasts in operational water management.