Flood Forecasting

Flood forecasting models are a key component in many flood warning systems, helping to extend warning lead times, thereby giving communities and emergency responders more time to take action. Typical forecasting techniques include rainfall-runoff, flow routing and hydrodynamic models and simpler empirical approaches such as level-to-level correlations. Data-driven and machine learning techniques are increasingly used too. Models are usually operated within a real-time forecasting system which gathers data, schedules model runs and prepares forecast products. Data assimilation and post-processing techniques are also widely used to help to improve forecasts along with hydrological ensemble prediction systems to provide estimates for forecast uncertainty. This chapter provides an introduction to these topics for riverine flood forecasting including three particular applications, namely continental and global scale flood forecasting systems, snowmelt forecasting in colder regions and forecasting water levels in estuaries. Key flood warning concepts are also introduced with examples of operational flood forecasting and warning systems from the Netherlands and Nepal.