Very high spatial and temporal resolution rainfall data for accurate flood inundation modelling

Abstract. High-quality rainfall data are crucial for various climatological and hydrological applications, especially in detailed modelling. However, obtaining precipitation data with fine spatiotemporal resolution is often challenging due to the limited availability of sub-daily point measurements and the sparse distribution of rainfall stations in many regions. This paper presents and demonstrates a method to generate the Commonwealth Scientific and Industrial Research Organization Hourly Rainfall (CHRain) dataset, which provides hourly and 1 km gridded rainfall surfaces for hydrological/hydrodynamic modelling. The method applies thin-plate spline interpolation to generate rainfall surfaces using hourly input time series obtained from hourly rainfall stations, and from daily data disaggregated into hourly intervals based on patterns observed in nearby hourly rainfall stations, and also guided by continuous radar images. The method is used to represent rainfall patterns and amounts from 2007 to 2022 in the Richmond River catchment in New South Wales, Australia. The CHRain dataset is compared with hourly measurements and other gridded datasets currently available in Australia. The correlation coefficient of 0.948 shows that the CHRain dataset can adequately reproduce the patterns of hourly rainfall measurements. The spatial and temporal analyses also indicate that the CHRain dataset outperforms other gridded datasets in representing the sub-grid distribution as well as the daily and hourly variation of rainfall across the study area. These are all essential for capturing the spatiotemporal characteristics of flood inundation in the study area which is frequented by disastrous flood events. The proposed method opens an opportunity to develop high resolution spatiotemporal rainfall datasets for other regions.