Real-time Flood Forecasting by Considering the Rain-burst Effect
Date Issued
2012
Date
2012
Author(s)
Wan, Been-Lih
Abstract
In recent years, the extreme rainfall events become more and more so that result in many flood disasters that make residents’ lives and property suffered a serious threat. In order to reduce flood damage, real-time flood forecasting has become an important research topic.
Research analysis was processed with flood events of Tseng-Wen Reservoir Watershed and Chi-Lan River basin. This study constituted several forecasting models of hourly stream discharge based on AR(2) model and Naïve model, and correct the problem of forecasting time lag phenomenon by considering rainfall data.
The discussion of rainfall data is divided in two parts. First part is that discuss the relationship between increment of rainfall and increment of discharge. By identify the increment of rainfall (rain-burst) which can make discharge significantly increase in a short time, we can establish the function of relationship between increment of rainfall and increment of discharge and combined with AR(2) model to correct the problem of forecasting time lag phenomenon which result from rain-burst effect. Second part is that apply the concept of unit hydrograph to establish response function between rainfall difference and discharge difference by linear regression, use data of rainfall difference before prediction time to estimate discharge difference on prediction time and combined with Naïve model to forecast hourly discharge. By considering the trend of rainfall variations, significantly improve the problem of forecasting time lag phenomenon.
The results of research shows that AR(2) model by considering the rain-burst effect can improve the problem of forecasting time lag phenomenon and enhance CP value by reducing the prediction error on peak time. And the performance of Naïve model which combined with response function is significantly better than other models. This result demonstrates that considering the trend of rainfall variations is very effective to improve the problem of forecasting time lag phenomenon.
Subjects
Time series
AR(2) model
Na?ve model
Rain-burst effect
Unit hydrograph
Response function
Type
thesis
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