Downscaling the Z–R relationship and bias correction solution for flash flood assessment in a data-scarce basin, Thailand

Weather radar is a form of alternative indirect rainfall measurement for use in mitigating flash flood hazards. It is a challenging task to obtain accurate radar rainfall data without integration with automatic rain gauge networks. This paper investigated transformation equations to convert the calibrated daily Z–R relationship to the sub-hourly scale and proposed optional schemes for downscaling the daily bias adjustment factor into 15 min resolution scale to produce a high-resolution radar rainfall product for flash flood modelling. Radar reflectivity data from three radar stations in Thailand and their corresponding daily gauge rainfall data were used in the analysis. Two bias adjustment schemes (DMFB and DS_DMFB), accounting for the temporal variation, and one spatiotemporal scheme (SPTB_IDS) were used to generate three corresponding rainfall datasets for the unified river basin simulator (URBS) model to simulate flood hydrographs in the Tubma basin, Thailand. The results showed that combining the proposed 15-min Z–R scaling equation and the SPTB_IDS produced the most reliable radar rainfall amount leading to an increase in the accuracy of flood modelling with the lowest uncertainty. This indicated that the temporal downscaling solution together with spatial interpolation technique for sub-hourly radar rainfall assessment could benefit flash flood simulation in a data-scarce basin. HIGHLIGHTS Daily gauge rainfall cannot detect a variation of sub-hourly intense rainfall. This paper proposed alternative techniques to downscale the Z-R relationship and daily data and combine it with radar observations to synthesize 15-min radar rainfall data.; Results show the benefit of the downscaled Z-R relationship and the most complicated radar bias adjustment method substantially improved the accuracy of flood estimates and reduced uncertainties in the flash flood modelling.;