This study examined the effect of the flood forecasting accuracy based on hydrological data quality control. Initially, several improvement methods suggested for the hydrological data quality based on the investigation of the status and problems of ...
This study examined the effect of the flood forecasting accuracy based on hydrological data quality control. Initially, several improvement methods suggested for the hydrological data quality based on the investigation of the status and problems of hydrological survey facilitation, data transmission and process. All of the rainfall and water level stations of the Han River were chosen and a 10-year data was collected to analyze the effect of the improvement methods. Each station data were analyzed, during the flood season, monthly and yearly. Consequently, hydrological data quality improved significantly after applying the suggested methods.
Moreover, there is a direct correlation between estimation results of the basin or precipitation and data quality control of each rainfall station by analysis of quantitative traits. The Storage Function and FLDWAV model are applied to simulate the flood events which occurred in the Han River basin. The parameter calibration are minimized to evaluate the effect of hydrological data quality control. The case studies are divided before and after the establishment of hydrological data quality control improvement. This study carefully analyzes the forecasting results of rainfall data quality control between raw data and calibration data respectively. Furthermore, the accuracy of flood forecasting based on water level data quality control compares with upstream and downstream water level station.
This research provides the ways of improving hydrological data quality control such as the installation of dual measurement and upgrading the transmission system. In addition, those contribute not only to the good quality of data production but also accuracy of model performance. Therefore, it is necessary to obtain a high quality of hydrological data for accurate flood forecasting result instead of concentrating on forecast model’s development and parameter calibration.