Abstract
The study of climate change adaptation plans for drainage infrastructure in a small country as that of Singapore, rainfall projections on the time scale of minutes and on the spatial scales of 1 km are deemed appropriate In this paper, we introduce an application of radar-based stochastic downscaling for rainfall projections at high temporal and spatial resolutions. The input for stochastic model is derived from a Regional Climate Model. The sub-hourly extreme rainfall intensity derived from stochastic model outputs was validated against observed rain-gauge data over the historical period. Considering the advantage in computational efficiency of the stochastic downscaling method, thousand scenarios of rainfall projections at very high temporal and spatial resolution were generated. The implication of this approach is that, from these stochastically downscaled time series of rainfall, it is possible to study future sub-hourly extreme rainfall intensities which would be useful to address issue of flash floods/drainage systems.
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Acknowledgements
We wish to thank Dr. Bhupendra Raut, formerly, Monash University and Dr. Alan Seed, Bureau of Meteorology, Australia, for their advice and guidance in the application of the HiDRUS model. We also thank the Public Utilities Board (PUB), Singapore, for providing the research grant towards investigating the impacts of extreme rainfall on drainage designs, under which this study was performed. We thank the Centre of Climate Research Singapore (CCRS) for providing us with the radar data and the station rainfall data for this study.
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Nguyen, N.S., Liu, J., Raghavan, S.V. et al. Deriving high spatiotemporal rainfall information over Singapore through dynamic-stochastic modelling using ‘HiDRUS’. Stoch Environ Res Risk Assess 35, 1453–1462 (2021). https://doi.org/10.1007/s00477-020-01912-y
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DOI: https://doi.org/10.1007/s00477-020-01912-y