CLUSTERING STOCHASTIC POINT PROCESS MODEL FOR FLOOD RISK ANALYSIS

Since the introduction into flood risk analysis, the partial duration series method has gained increasing acceptance as an appealing alterna tive to the annual maximum series method. However, when the base flow is low, there is clustering in the flood peak or flow volume point pro cess. In this case, the general stochastic point process model is not suitable to risk analysis. Therefore, two types of models for flood ri sk analysis are derived on the basis of clustering stochastic point pr ocess theory in this paper. The most remarkable characteristic of thes e models is that the flood risk is considered directly within the time domain. The acceptability of different models are also discussed with the combination of the flood peak counted process in twenty years at Yichang station on the Yangtze river. The result shows that the two ki nds of models are suitable ones for flood risk analysis, which are mor e flexible compared with the traditional flood risk models derived on the basis of annual maximum series method or the general stochastic po int process theory.