Document Type

Presentation

Publication Date

8-1-2014

Abstract

Destructive floods occurred more frequently in mountainous regions in China in recent years. However, the meteorological and hydrological station network in such regions is usually poor, and no long-series observations are available. Therefore, it is difficult to determine the hydrological parameters for flood discharge and stage forecast. This paper aims to propose an automatic frequency-based flood forecast framework from numerical weather prediction (NWP) using a Service Oriented Architecture (SOA). The proposed framework has 4 main steps. First, historical flood discharge is simulated by using a distributed hydrological model and satellite-derived rainfall dataset (e.g., the CMORPH and the TRMM), and the relationship between flood frequency and simulated flood discharge (i.e., the frequency curve) is established for each river reach. Second, by taking the advantages of the highly automatic SOA technology, the predicted rainfall data from the NWP (e.g., the TIGGE ensemble) are downloaded and interpreted automatically in real time. Third, a distributed hydrological model is automatically executed in the SOA environment to predict flow discharges of each river reach. And finally, the flood frequency is obtained from the simulated flow discharges by looking up the frequency curves, and warning information of possible floods is generated for potential sufferers. By using Web service in a social network, users can be informed such warning information at any time, and can make better preparation for the possible floods. Along with the real-time updates of the NWP, the latest warning information will always be available for users. From a sample demonstration, it can be concluded that the frequency-based flood forecast from the NWP is highly useful to enhance user awareness of flood risk, and the SOA and social network techniques are regarded as a feasible way for developing the automatic system.

Comments

Session R41, Flood Early Warning Systems

 
 

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