Impact of Bias-Correction Methods in Assessing the Potential Flood Frequency Change in the Bago River
Ralph Allen E. Acierto*,, Akiyuki Kawasaki*, and Win Win Zin**
*The University of Tokyo
7-3-1 Hongo, Bunkyo, Tokyo 113-8656, Japan
**Yangon Technological University, Yangon, Myanmar
The increasing flood risks in the Bago River due to rapid urbanization and climate change have great implications on the local development and quality of life in the basin. Therefore, the current flood hazard and potential future changes in flooding due to climate change must be assessed. This study investigates the potential flood frequency change in the Bago River and its sensitivity to the bias-correction method used in climate projections from the downscaled Global Climate Model (GCM) output. A pseudo-global warming method using MIROC5 RCP 8.5 was employed to produce 12-km 30-y historical and future climate projections. Empirical quantile mapping (EQM), gamma quantile mapping (GQM), and the multiplicative scaling method (SCM) were used for bias-correcting the rainfall input of the water-energy budget distributed hydrological model (WEB-DHM). The impacts of bias-correction methods used in reproducing the annual maximum series in the frequency analysis are sensitive to the trend of potential future changes in flood discharge frequency estimation. All methods exhibited decreases in the flood peak discharge for 50-yr and 100-yr flood predictions, which may primarily be due to the MIROC5 GCM used. However, the variation in the magnitude of the change is wide. This demonstrates the uncertainty of the frequency analysis for flood magnitude due to the employed bias-correction method. This uncertainty has significant implications on risk quantification conducted using downscaled climate projections. The effect of the uncertainty of the bias-correction method on the annual maximum rainfall time series should be communicated properly when conducting risk and hazard assessment studies.
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