Enhancement of Satellite Precipitation Estimations with Bias Correction and Data-Merging Schemes for Flood Forecasting

Abstract

This study investigates the capability of both quantile mapping (QM) bias correction and kriging merging techniques to improve precipitation accuracy of Tropical Rainfall Measuring Mission (TRMM) and Integrated Multisatellite Retrievals for the Global Precipitation Measurement (IMERG) satellite estimations over the Langat River Basin, an important river basin in Malaysia as it is the main source of potable water supply to Kuala Lumpur, in the 5-year period (2014–2018). This analysis also integrates both techniques to investigate whether the estimations can be further improved. Findings show that the estimations that undergo QM first followed by kriging merging (QK-TRMM and QK-IMERG) give significant improvement at almost all aspects of rainfall and streamflow comparison. At point-to-pixel rainfall comparison, around 50% improvement can be seen in both time series– and frequency-based statistics as well as an able to perform with a coefficient of correlation (CC) over 0.80 in terms of areal rainfall. The study performs streamflow simulation by employing the hydrological modeling system (HEC-HMS) to validate the performance of raw and enhanced satellite estimations for the 2014–2015 extreme flood events. Both QK-TRMM and QK-IMERG show a great improvement in the overall streamflow simulation with a Nash–Sutcliffe efficiency (NSE) of more than 0.70. The results reveal that the newly proposed bias correction method (merging of the QM and kriging methods) has significantly contributed to the improvement of precipitation estimation, which is crucial in water resources planning and flood forecasting.