Preprints
https://doi.org/10.5194/hess-2024-228
https://doi.org/10.5194/hess-2024-228
14 Oct 2024
 | 14 Oct 2024
Status: this preprint is currently under review for the journal HESS.

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

Chi Nguyen, Jai Vaze, Cherry May R. Mateo, Michael F. Hutchinson, and Jin Teng

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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Chi Nguyen, Jai Vaze, Cherry May R. Mateo, Michael F. Hutchinson, and Jin Teng

Status: open (until 09 Dec 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on hess-2024-228', Sivarajah Mylevaganam, 11 Nov 2024 reply
  • CC2: 'Comment on hess-2024-228', Sivarajah Mylevaganam, 11 Nov 2024 reply
  • CC3: 'Comment on hess-2024-228', Sivarajah Mylevaganam, 12 Nov 2024 reply
  • CC4: 'Comment on hess-2024-228', Sivarajah Mylevaganam, 13 Nov 2024 reply
  • CC5: 'Comment on hess-2024-228', Sivarajah Mylevaganam, 13 Nov 2024 reply
  • CC6: 'Comment on hess-2024-228', Sivarajah Mylevaganam, 14 Nov 2024 reply
Chi Nguyen, Jai Vaze, Cherry May R. Mateo, Michael F. Hutchinson, and Jin Teng
Chi Nguyen, Jai Vaze, Cherry May R. Mateo, Michael F. Hutchinson, and Jin Teng

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Short summary
The availability of high-resolution rainfall data is limited. This study presents a method to generate hourly and 1 km gridded rainfall data for detailed hydrodynamic flood modelling purposes, using point measurements and thin-plate spline interpolation. The analysis shows that the proposed dataset outperforms other gridded datasets in representing spatial distributions and daily and hourly variations of rainfall. The data is suitable for any study where high-resolution rainfall data is needed.