초록
영어
In this study, Mike11 will be used as the numerical model where a data assimilation method will be applied to it. This paper aims to gain an insight and understanding of data assimilation in flood forecasting models. It will start with a general discussion of data assimilation, followed by a description of the methodology and discussion of the statistical error forecast model used, which in this case is the linear regression. This error forecast model is applied to the water level forecast simulated by MIKE11 to produced improved forecast and validated against real measurements. It is found that there exists a phase error in the improved forecasts. Hence, 2 general formula are used to account for this phase error and they have shown improvement to the accuracy of the forecasts, where one improved the immediate forecast of up to 5 hours while the other improved the estimation of the peak discharge.
목차
1. Introduction
2. Model Description
2.1 WEKA
2.2 M I KE11 model
3. Data Assimilation
4. Methodology
4.1 Statistics
4.2 Accounting for Errors
5. Case Study
5.1 Overview of the River Networks
5.2 Rainfall Runoff Model
5.3 Data
5.4 Analysis Results
6. Conclusion
References