Anticipatory Water Management - Using Ensemble Weather Forecasts for Critical Events: Unesco-Ihe PhD Thesis

ISBN-13: 9781138474680 / Angielski / Twarda / 2018

Schalk-Jan Andel

Opis

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.

Spis treści

FOREWORD ACKNOWLEDGEMENTS SUMMARY 1 INTRODUCTION 1.1 BACKGROUND 1.1.1 Hydroinformatics and Integrated Water Resources Management 1.1.2 Management of extreme events 1.1.3 Operational water management 1.1.4 Benefits of increased forecast horizon 1.1.5 Use of weather forecasts 1.1.6 Ensemble forecasts 1.2 ANTICIPATORY WATER MANAGEMENT 1.3 HYPOTHESES AND OBJECTIVES 1.4 READER 2 ANTICIPATORY WATER MANAGEMENT 2.1 INTRODUCTION 2.2 OPERATIONAL WATER MANAGEMENT 2.2.1 Definition 2.2.2 Components of operational water management 2.2.3 Water system control 2.2.4 Reservoirs and polders 2.2.5 Flood early warning and control 2.2.6 Challenges in operational water management 2.3 WEATHER FORECASTING AND ENSEMBLE PREDICTIONS 2.3.1 Monitoring systems 2.3.2 From hand-drawn weather maps to numerical prediction 2.3.3 From deterministic to probabilistic forecasts 2.3.4 Ensemble Prediction Systems 2.3.5 Challenges in using weather forecasts for water management 2.4 MODELLING CONTROLLED WATER SYSTEMS 2.4.1 Definitions 2.4.2 Model components 2.4.3 Water system state prediction 2.4.4 Challenges in modelling controlled water systems 2.5 DECISION MAKING WITH UNCERTAINTY 2.5.1 Uncertainty 2.5.2 Risk 2.5.3 Threshold-based decision rules for Ensemble Prediction Systems 2.5.4 Cost-benefit analysis 2.5.5 Decision Support Systems for Anticipatory Water Management 2.6 KNOWLEDGE GAPS AND HYPOTHESES 3 FRAMEWORK FOR DEVELOPING ANTICIPATORY WATER MANAGEMENT (AWM) 3.1 INTRODUCTION 3.2 ESTABLISHING THE NEED AND POTENTIAL FOR AWM 3.2.1 For which events is AWM needed 3.2.2 Potential for anticipatory management action 3.3 VERIFICATION ANALYSIS 3.3.1 Product selection: time scales, spatial scales 3.3.2 Continuous simulation of the real-time AWM forecasting system 3.3.3 Event based verification of a range of decision rules for AWM 3.4 MODELLING CONTROLLED WATER SYSTEMS 3.4.1 Input data based on end-use of model 3.4.2 Framework for modelling controlled water systems 3.5 STRATEGIES FOR ANTICIPATORY WATER MANAGEMENT 3.5.1 Rule-based 3.5.2 Pre-processing of ensemble forecasts to deterministic forecast 3.5.3 Risk-based 3.6 COST-BENEFIT OF SELECTED AWM STRATEGIES 3.6.1 Dynamic cost-benefit analysis 3.6.2 Sources of damage 3.6.3 Anticipatory Water Management modelling 3.7 OPTIMISATION OF ANTICIPATORY WATER MANAGEMENT 3.7.1 Objectives 3.7.2 Parameterisation of AWM strategies 3.7.3 Optimisation using perfect forecasts 3.7.4 Optimisation with actual forecasts 3.8 DECISION MAKING FOR POLICY ADOPTION OF AWM 3.8.1 What-if analysis 3.8.2 Re-analysis era 3.9 FRAMEWORK FOR DEVELOPING ANTICIPATORY WATER MANAGEMENT 4 CASE STUDY 1 - RIJNLAND WATER SYSTEM 4.1 INTRODUCTION 4.2 PROBLEM DESCRIPTION 4.3 DATA 4.4 WATER SYSTEM CONTROL MODEL 4.4.1 Model structure 4.4.2 Control strategy 4.4.3 Model calibration 4.4.4 Model validation 4.4.5 Visualise what is not known and explain 4.4.6 Modelling the unknown phenomena 4.4.7 Final model results 4.4.8 Discussion 4.5 ENSEMBLE FORECASTS VERIFICATION 4.5.1 Precipitation ensemble forecasts archive 4.5.2 Water level hindcasts 4.5.3 Event based verification for water managers 4.5.4 Precipitation and water level thresholds 4.5.5 Presently used precipitation threshold for anticipatory pumping 4.5.6 3-Day accumulated precipitation threshold for selected events 4.5.7 5-Day accumulated precipitation threshold for selected events 4.5.8 Discussion 4.6 ANTICIPATORY WATER MANAGEMENT STRATEGY DEVELOPMENT 4.7 COST-BENEFIT OF SELECTED AWM STRATEGIES 4.7.1 Water level - damage function 4.7.2 Inter-comparison of costs for selected strategies 4.8 OPTIMISATION OF ANTICIPATORY WATER MANAGEMENT STRATEGY 4.8.1 Optimisation with perfect forecasts 4.8.2 Optimisation with actual forecasts 4.9 ADOPTION OF AWM IN OPERATIONAL MANAGEMENT POLICY 5 CASE STUDY 2 - UPPER BLUE NILE 5.1 INTRODUCTION 5.2 PROBLEM DESCRIPTION 5.3 DATA 5.3.1 Geographical data 5.3.2 Meteorological data 5.3.3 Streamflow data 5.4 HYDROLOGICAL MODEL 5.4.1 Model set-up 5.4.2 Calibration and validation 5.5 ENSEMBLE FORECASTS VERIFICATION 5.5.1 Event selection 5.5.2 Ensemble precipitation hindcasts 5.5.3 Ensemble streamflow hindcasts 5.5.4 Verification analysis 5.5.5 Statistical verification 5.5.6 Comparison by visual inspection 5.5.7 Flood early warning verification 5.6 ANTICIPATORY MANAGEMENT STRATEGY DEVELOPMENT 5.7 ADOPTION OF AWM IN OPERATIONAL MANAGEMENT POLICY 6 CONCLUSIONS AND RECOMMENDATIONS 6.1 CONTRIBUTIONS TO ANTICIPATORY WATER MANAGEMENT 6.2 DISCUSSION OF THE HYPOTHESES 6.3 CONCLUSIONS 6.4 RECOMMENDATIONS FOR MANAGEMENT PRACTICE 6.5 RECOMMENDATIONS FOR FURTHER RESEARCH REFERENCES LIST OF FIGURES ABOUT THE AUTHOR SAMENVATTING

O autorze

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.