Flood disaster due to prolonged heavy rainfall had caused millions ringgit of property losses, infrastructure damages and numerous deaths in the east coast region of Peninsular Malaysia. One of the efforts taken to improve disaster preparedness in this region is by enhancing the flood forecasting and warning system (FFWS) using rainfall input from weather radar. Weather radar has the advantage of its ability to provide good spatial and temporal resolution of rainfall estimates but comes with inherent associated errors. In this study, the radar rainfall estimates were improved by climatological calibration of reflectivity-rain (Z-R) relationships for Pahang river basin. The reflectivity data for period of one year from Kuantan radar station and the hourly rainfall depths at 67 rainfall stations located in the basin for the same periods were used. Correlation analysis between radar and gauged rainfall indicates that the further the distance from the radar, the weaker the R2 coefficient value. Two Z-R equations were derived using optimization method for distance (1) 0-100 km and (2) above 100 km from Kuantan radar. The results in the form of Z = 24R1.7 and Z =5R1.6 represents the average relationship for Kuantan radar for distance (1) and (2). The radar rainfall estimates using the newly derived climatological Z-R equations enhanced the FFWS for Pahang river basin.
The 2021–2022 flood is one of the most serious flood events in Malaysian history, with approximately 70,000 victims evacuated daily, 54 killed and total losses up to MYR 6.1 billion. From this devastating event, we realized the lack of extreme precipitation and flood inundation information, which is a common problem in tropical regions. Therefore, we developed a Rapid Extreme TRopicAl preCipitation and flood inundation mapping framEwork (RETRACE) by utilizing: (1) a cloud computing platform, the Google Earth Engine (GEE); (2) open-source satellite images from missions such as Global Precipitation Measurement (GPM), Sentinel-1 SAR and Sentinel-2 optical satellites; and (3) flood victim information. The framework was demonstrated with the 2021–2022 Malaysia flood. The preliminary results were satisfactory with an optimal threshold of five for flood inundation mapping using the Sentinel-1 SAR data, as the accuracy of inundated floods was up to 70%. Extreme daily precipitation of up to 230 mm/day was observed and resulted in an inundated area of 77.43 km2 in Peninsular Malaysia. This framework can act as a useful tool for local authorities and scientists to retrace the extreme precipitation and flood information in a relatively short period for flood management and mitigation strategy development.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.