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Flash floods are not only the deadliest weather-related hazard but also one of the leading challenges with which governments and societies need to cope. Flash floods occur within a very limited time, which is insufficient to enable effective warnings and preparedness. Flash floods have become, for many reasons, the most frequent form of natural disaster in Malaysia, considerably affecting humans, property, and the economy. Modeling flash flood phenomena in the tropics is challenging due to the high topographic and meteorological complexity of these regions. The uncertain definition boundary of the monsoon flood and the multidisciplinary nature of flash flood studies also increase the challenge of the reviewing process. In this study, a systematic methodology was developed to review flash floods in Malaysia by considering all the possible related issues. This study revealed a gap in the data analysis of flash floods and that related studies in Malaysia are still not highly developed. Accordingly, the creation of a comprehensive Malaysian flash flood dataset is recommended to advance flash flood studies, modeling, and forecasting. Rainfall analysis based on Global Precipitation Measurement and Tropical Rainfall Measuring Mission data of different intensities also confirmed the high variability of rainfall in Malaysia. The highest variability in the hourly-based rainfall dataset was observed in the central region. The information and findings presented here will be useful for interested hydrologists and decision-makers by enabling better water management. Additionally, the proposed recommendations for future research could pave the way for a better understanding of flash floods in Malaysia, and the method could be applied in different river basins worldwide. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]