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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 1
Hydrol. Earth Syst. Sci., 22, 727–756, 2018
https://doi.org/10.5194/hess-22-727-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 22, 727–756, 2018
https://doi.org/10.5194/hess-22-727-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 26 Jan 2018

Research article | 26 Jan 2018

Censored rainfall modelling for estimation of fine-scale extremes

David Cross1, Christian Onof1, Hugo Winter2, and Pietro Bernardara3 David Cross et al.
  • 1Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK
  • 2EDF Energy R&D UK Centre, Interchange, 81–85 Station Road, Croydon, CR0 2RD, UK
  • 3CEREA, EDF R&D – ENPC, 6 quai Watier, 78400 Chatou, France

Abstract. Reliable estimation of rainfall extremes is essential for drainage system design, flood mitigation, and risk quantification. However, traditional techniques lack physical realism and extrapolation can be highly uncertain. In this study, we improve the physical basis for short-duration extreme rainfall estimation by simulating the heavy portion of the rainfall record mechanistically using the Bartlett–Lewis rectangular pulse (BLRP) model. Mechanistic rainfall models have had a tendency to underestimate rainfall extremes at fine temporal scales. Despite this, the simple process representation of rectangular pulse models is appealing in the context of extreme rainfall estimation because it emulates the known phenomenology of rainfall generation. A censored approach to Bartlett–Lewis model calibration is proposed and performed for single-site rainfall from two gauges in the UK and Germany. Extreme rainfall estimation is performed for each gauge at the 5, 15, and 60 min resolutions, and considerations for censor selection discussed.

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Extreme rainfall is one of the most significant natural hazards. However, estimating very large events is highly uncertain. We present a new approach to construct intense rainfall using the structure of rainfall generation in clouds. The method is particularly effective at estimating short-duration extremes, which can be the most damaging. This is expected to have immediate impact for the estimation of very rare downpours, with the potential to improve climate resilience and hazard preparedness.
Extreme rainfall is one of the most significant natural hazards. However, estimating very large...
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