River Flood Ensemble Forecast Model
Date Issued
2012
Date
2012
Author(s)
Yu, Szu-Liang
Abstract
The special geographical and meteorological environment induced lots of natural disasters such as typhoon and flood in Taiwan. Emergency response and flood evacuation are the major non-structural measures for flood mitigation. Therefore, an accurate flood forecasting model is an indispensable tool for the decision of disaster management agencies. Probabilistic forecasting of flood stage can provide not only the most likely water level, but also the possible range, which offer the reference of a variety of potential situations for decision-makers.
Based on one-dimensional dynamic wave theorem, an ensemble forecast technique has been developed in this study by considering uncertainties factors including initial condition, boundary condition, and Manning’s coefficient. The original of dynamic model is a deterministic model which converts to probabilistic forecasting model with the ensemble forecasting. The join data assimilation using the ensemble Kalman filter and back-propagation neural network are employed on gage stations which can offer better feedback estimate and model accuracy.
The model is applied to the Tamsui River basin. Two typhoon events of Weipa(2007) and Sinlaku (2008) are used as model validation. The simulated results show that flood stage of the probabilistic forecasting is better accuracy than that of the deterministic forecasting. Based on the probability forecast of 95% confidence interval, the most of the observed level were located in the predicted range. From the comparison of the actual hit ratio of the two typhoon events, it can be found that the 89.5% and 78.8% of observed level fell at prediction range of confidence interval, which shown that forecast range is not enough and underestimate of the uncertainty. This phenomenon is obvious especially in the river midstream. It can be seen that the more factors of uncertainty is needed for further study.
Subjects
Ensemble forecasting
Uncertainty
Flood forecasting
Dynamic routing model
Ensemble Kalman Filter
Data Assimilation
Type
thesis
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