Real-Time Flood Warning System Application

Ray Shyan Wu, You Yu Sin, Jing Xue Wang, Yu Wen Lin, Hsing Chuan Wu, Riyan Benny Sukmara, Lina Indawati, Fiaz Hussain

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The reliability of weather radar data in real-time flood forecasting and early warning system remain ambivalent due to high uncertainty in Quantitative Precipitation Forecasts (QPF). In this study, a methodology is presented with the objective to improve the flood forecasting results with the application of radar rainfall calculated in three different ways. The QPF radar rainfall forecast data of four typhoon events in Fèngshān River Basin, Taiwan, were simulated using the WASH123D numerical model. The simulated results were corrected using a physical real-time correction technique and compared with direct simulation without correction for all three QPF calculation methods. According to model performance evaluation criteria, in the third method of QPF calculation, flood peak error was the lowest in all three methods, indicating better results for flood forecasting and can be used for flood early warning systems. The impact of the real-time correction technique was assessed using mass balance analysis. It was found that flow change is between 16% and 42% from direct simulation, indicating being on the safe side in case of a flood warning. However, the impact of the real-time physical correction on the water level itself is in a reasonable range. Still, QPF rainfall correction/calculation is more important to obtain accurate results for flood forecasting. Therefore, the application of real-time correction to correct the model water level has a certain degree of credibility, which is the mass balance of the model. This approach is recommended for flood forecasting early warning systems.

Original languageEnglish
Article number1866
JournalWater (Switzerland)
Volume14
Issue number12
DOIs
StatePublished - 1 Jun 2022

Keywords

  • flood forecasting
  • QPF radar rainfall
  • real-time correction
  • WASH123D

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