Newcastle University

Pre Requisite Comment

None

Co Requisite Comment

None

Aims

Flood risk is increasing due to the growth in people and property living in flood plains, and the possible increase in flood hazard associated with climate change. Real-time flood forecasting and warning has become an important component of flood risk management strategies that seek to mitigate the impacts of floods on communities at risk by issuing timely and accurate forecasts and warnings of impending flooding.
The aims of the module are therefore:

1. To describe the principles and practice of real-time flood forecasting and warning (RTFFW);
2. To illustrate current practice with examples of RTFFW in the UK and internationally;
3. To define future RTFFW best practice and how to move towards it.

Outline Of Syllabus

The course covers the underlying principles and theory underlying flood forecasting methods, and how flood forecasts are translated into flood warnings, taking account of the inherent uncertainty in the flood forecasts. Case studies are presented to illustrate current real-time flood forecasting and warning practice in the UK and internationally, and how this is evolving to improve decision making in issuing flood warnings.
Day 1
Overview. Role of real time flood forecasting and warning in flood risk management.
Benefits, requirements and performance      
Developing and using the flood forecast. Physical systems, processes, lags,
Component models, forecast uncertainty      

Day 2
Rainfall Forecasting: rainfall, radar, models      
Real-time Flood Forecasting Methods I: Empirical, transfer function and ANN models      
Real-time Flood Forecasting Methods II: Rainfall runoff models: conceptual and
physically-based      
Real-time Flood Forecasting Methods III: Flood routing models      

Day 3
Coastal Flooding: storm surge forecasting      
NFFS: the EA National Flood Forecasting System: overview and regional application      
Computer practical: rainfall-runoff modelling
     
Day 4
The Joint Environment Agency and Met Office Flood Forecasting Centre for England and Wales      
The Morpeth Flood: physical and emergency response aspects      
Real-time Flood Forecasting Methods IV: Predictive uncertainty and its use to improve decision-making      
VIRTUAL FLOOD: A simulated flood event and warning response exercise                  
           
Day 5
Real-time Flood Forecasting Methods V : Data Assimilation 1: Application of the Kalman Filter and Ensemble Kalman Filter
Real-time Flood Forecasting Methods V: Data Assimilation 2: Combining data from different sensors (Radar/raingauges/satellite)
Case Studies of international real time flood forecasting and warning systems
Review of course and wrap up

Intended Knowledge Outcomes

At the end of this module, students should have:-

1. An understanding of requirements for RTF in different hydrological regimes
2. Hands on experience of variety of RTF systems, their characteristics and capabilities

Intended Skill Outcomes

To deploy the understanding of requirements and capabilities of methods in operating RTF systems.
Competence to specify suitable RTF systems.

Teaching Rationale And Relationship

To impart the basic theoretical and practical understanding represented by the knowledge outcomes via a mix of self learning and formal teaching, including formal lecture presentations and discussions/practicals with active student participation. Lectures introduce theory and concepts, which are then exemplified in computer workshops using specialist packages and tailored data sets. For real time forecasting, the theory underpinning modern practice is taught in lectures and then tested in practical sessions.

Assessment Rationale And Relationship

The written exam assesses understanding of theory and practice of real-time forecasting, together with detailed knowledge of existing systems.
     
The case study sets a real-life task in flood warning, using a computer package, to be tackled partly during the workshop sessions and partly outside. The assignment tests (1) understanding of theory and its relation to practice, (2) initiative in using and selecting methods and data (3) mastery of methods and software.

General Notes

N/A