Spatial and temporal scaling of sub-daily extreme rainfall for data sparse places

Wilby, R.L.; Dawson, C.W.; Yu, D.; Herring, Z.; Baruch, A.; Ascott, M.J.; Finney, D. L.; Macdonald, D.M.J.; Marsham, J.H.; Matthews, T.; Murphy, C.. 2023 Spatial and temporal scaling of sub-daily extreme rainfall for data sparse places. Climate Dynamics, 60. 3577-3596.

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Global efforts to upgrade water, drainage, and sanitation services are hampered by hydrometeorological data-scarcity plus uncertainty about climate change. Intensity–duration–frequency (IDF) tables are used routinely to design water infrastructure so offer an entry point for adapting engineering standards. This paper begins with a novel procedure for guiding downscaling predictor variable selection for heavy rainfall simulation using media reports of pluvial flooding. We then present a three-step workflow to: (1) spatially downscale daily rainfall from grid-to-point resolutions; (2) temporally scale from daily series to sub-daily extreme rainfalls and; (3) test methods of temporal scaling of extreme rainfalls within Regional Climate Model (RCM) simulations under changed climate conditions. Critically, we compare the methods of moments and of parameters for temporal scaling annual maximum series of daily rainfall into sub-daily extreme rainfalls, whilst accounting for rainfall intermittency. The methods are applied to Kampala, Uganda and Kisumu, Kenya using the Statistical Downscaling Model (SDSM), two RCM simulations covering East Africa (CP4 and P25), and in hybrid form (RCM-SDSM). We demonstrate that Gumbel parameters (and IDF tables) can be reliably scaled to durations of 3 h within observations and RCMs. Our hybrid RCM-SDSM scaling reduces errors in IDF estimates for the present climate when compared with direct RCM output. Credible parameter scaling relationships are also found within RCM simulations under changed climate conditions. We then discuss the practical aspects of applying such workflows to other city-regions.

Item Type: Publication - Article
Digital Object Identifier (DOI):
ISSN: 0930-7575
Additional Keywords: GroundwaterBGS, Groundwater
Date made live: 17 Nov 2022 09:37 +0 (UTC)

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