Methods and tools to support real time risk-based flood forecasting - a UK pilot application
Flood managers have traditionally used probabilistic models to assess potential flood risk for strategic planning and non-operational applications. Computational restrictions on data volumes and simulation times have meant that information on the risk of flooding has not been available for operation...
Gespeichert in:
Hauptverfasser: | , , , , , , , , , , |
---|---|
Format: | Tagungsbericht |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Flood managers have traditionally used probabilistic models to assess potential flood risk for strategic planning and non-operational applications. Computational restrictions on data volumes and simulation times have meant that information on the risk of flooding has not been available for operational flood forecasting purposes. In practice, however, the operational flood manager has probabilistic questions to answer, which are not completely supported by the outputs of traditional, deterministic flood forecasting systems. In a collaborative approach, HR Wallingford and Deltares have developed methods, tools and techniques to extend existing flood forecasting systems with elements of strategic flood risk analysis, including probabilistic failure analysis, two dimensional flood spreading simulation and the analysis of flood impacts and consequences. This paper presents the results of the application of these new operational flood risk management tools to a pilot catchment in the UK. It discusses the problems of performing probabilistic flood risk assessment in real time and how these have been addressed in this study. It also describes the challenges of the communication of risk to operational flood managers and to the general public, and how these new methods and tools can provide risk-based supporting evidence to assist with this process. |
---|---|
ISSN: | 2267-1242 2555-0403 2267-1242 |
DOI: | 10.1051/e3sconf/20160718019 |