Anticipatory Water Management – Using ensemble weather forecasts for critical events UNESCO-IHE Phd Thesis

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.

FOREWORDACKNOWLEDGEMENTSSUMMARY 1 INTRODUCTION1.1 BACKGROUND1.1.1 Hydroinformatics and Integrated Water Resources Management1.1.2 Management of extreme events1.1.3 Operational water management1.1.4 Benefits of increased forecast horizon1.1.5 Use of weather forecasts1.1.6 Ensemble forecasts 1.2 ANTICIPATORY WATER MANAGEMENT1.3 HYPOTHESES AND OBJECTIVES1.4 READER 2 ANTICIPATORY WATER MANAGEMENT2.1 INTRODUCTION2.2 OPERATIONAL WATER MANAGEMENT2.2.1 Definition2.2.2 Components of operational water management2.2.3 Water system control2.2.4 Reservoirs and polders2.2.5 Flood early warning and control2.2.6 Challenges in operational water management2.3 WEATHER FORECASTING AND ENSEMBLE PREDICTIONS2.3.1 Monitoring systems2.3.2 From hand-drawn weather maps to numerical prediction2.3.3 From deterministic to probabilistic forecasts2.3.4 Ensemble Prediction Systems2.3.5 Challenges in using weather forecasts for water management2.4 MODELLING CONTROLLED WATER SYSTEMS2.4.1 Definitions2.4.2 Model components2.4.3 Water system state prediction2.4.4 Challenges in modelling controlled water systems2.5 DECISION MAKING WITH UNCERTAINTY2.5.1 Uncertainty2.5.2 Risk2.5.3 Threshold-based decision rules for Ensemble Prediction Systems2.5.4 Cost-benefit analysis2.5.5 Decision Support Systems for Anticipatory Water Management2.6 KNOWLEDGE GAPS AND HYPOTHESES 3 FRAMEWORK FOR DEVELOPING ANTICIPATORY WATER MANAGEMENT (AWM)3.1 INTRODUCTION3.2 ESTABLISHING THE NEED AND POTENTIAL FOR AWM3.2.1 For which events is AWM needed3.2.2 Potential for anticipatory management action3.3 VERIFICATION ANALYSIS3.3.1 Product selection: time scales, spatial scales3.3.2 Continuous simulation of the real-time AWM forecasting system3.3.3 Event based verification of a range of decision rules for AWM3.4 MODELLING CONTROLLED WATER SYSTEMS3.4.1 Input data based on end-use of model3.4.2 Framework for modelling controlled water systems3.5 STRATEGIES FOR ANTICIPATORY WATER MANAGEMENT3.5.1 Rule-based3.5.2 Pre-processing of ensemble forecasts to deterministic forecast3.5.3 Risk-based3.6 COST-BENEFIT OF SELECTED AWM STRATEGIES3.6.1 Dynamic cost-benefit analysis3.6.2 Sources of damage3.6.3 Anticipatory Water Management modelling3.7 OPTIMISATION OF ANTICIPATORY WATER MANAGEMENT3.7.1 Objectives3.7.2 Parameterisation of AWM strategies3.7.3 Optimisation using perfect forecasts3.7.4 Optimisation with actual forecasts3.8 DECISION MAKING FOR POLICY ADOPTION OF AWM3.8.1 What-if analysis3.8.2 Re-analysis era3.9 FRAMEWORK FOR DEVELOPING ANTICIPATORY WATER MANAGEMENT 4 CASE STUDY 1 - RIJNLAND WATER SYSTEM4.1 INTRODUCTION4.2 PROBLEM DESCRIPTION4.3 DATA4.4 WATER SYSTEM CONTROL MODEL4.4.1 Model structure4.4.2 Control strategy4.4.3 Model calibration4.4.4 Model validation4.4.5 Visualise what is not known and explain4.4.6 Modelling the unknown phenomena4.4.7 Final model results4.4.8 Discussion4.5 ENSEMBLE FORECASTS VERIFICATION4.5.1 Precipitation ensemble forecasts archive4.5.2 Water level hindcasts4.5.3 Event based verification for water managers4.5.4 Precipitation and water level thresholds4.5.5 Presently used precipitation threshold for anticipatory pumping4.5.6 3-Day accumulated precipitation threshold for selected events4.5.7 5-Day accumulated precipitation threshold for selected events4.5.8 Discussion4.6 ANTICIPATORY WATER MANAGEMENT STRATEGY DEVELOPMENT4.7 COST-BENEFIT OF SELECTED AWM STRATEGIES4.7.1 Water level - damage function4.7.2 Inter-comparison of costs for selected strategies4.8 OPTIMISATION OF ANTICIPATORY WATER MANAGEMENT STRATEGY4.8.1 Optimisation with perfect forecasts4.8.2 Optimisation with actual forecasts4.9 ADOPTION OF AWM IN OPERATIONAL MANAGEMENT POLICY 5 CASE STUDY 2 - UPPER BLUE NILE5.1 INTRODUCTION5.2 PROBLEM DESCRIPTION5.3 DATA5.3.1 Geographical data5.3.2 Meteorological data5.3.3 Streamflow data5.4 HYDROLOGICAL MODEL5.4.1 Model set-up5.4.2 Calibration and validation5.5 ENSEMBLE FORECASTS VERIFICATION5.5.1 Event selection5.5.2 Ensemble precipitation hindcasts5.5.3 Ensemble streamflow hindcasts5.5.4 Verification analysis5.5.5 Statistical verification5.5.6 Comparison by visual inspection5.5.7 Flood early warning verification5.6 ANTICIPATORY MANAGEMENT STRATEGY DEVELOPMENT5.7 ADOPTION OF AWM IN OPERATIONAL MANAGEMENT POLICY 6 CONCLUSIONS AND RECOMMENDATIONS6.1 CONTRIBUTIONS TO ANTICIPATORY WATER MANAGEMENT6.2 DISCUSSION OF THE HYPOTHESES6.3 CONCLUSIONS6.4 RECOMMENDATIONS FOR MANAGEMENT PRACTICE6.5 RECOMMENDATIONS FOR FURTHER RESEARCH REFERENCESLIST OF FIGURESABOUT THE AUTHORSAMENVATTING

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). In2004 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.