Preprints
https://doi.org/10.5194/egusphere-2023-2057
https://doi.org/10.5194/egusphere-2023-2057
23 Oct 2023
 | 23 Oct 2023
Status: this preprint is open for discussion.

Global estimates of 100-year return values of daily precipitation from ensemble weather prediction data

Florian Ruff and Stephan Pfahl

Abstract. High-impact river floods are often caused by very extreme precipitation events with return periods of several decades or centuries, and the design of flood protection measures thus relies on reliable estimates of the corresponding return values. However, calculating such return values from observations is associated with large statistical uncertainties due to the limited length of observational time series. Here, 100-year return values of daily precipitation are estimated on a global grid based on a large data set of model-generated precipitation events from ensemble weather prediction. In this way, the statistical uncertainties of the return values can be substantially reduced compared to observational estimates. In spite of a general agreement of spatial patterns, the model-generated data set leads to systematically higher return values than the observations in many regions, with statistically significant differences, for instance, over the Amazon, western Africa, the Arabian Peninsula and India. This may point to an underestimation of very extreme precipitation events in observations, which, if true, would have important consequences for practical water management.

Florian Ruff and Stephan Pfahl

Status: open (until 22 Dec 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Florian Ruff and Stephan Pfahl

Florian Ruff and Stephan Pfahl

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Short summary
High-impact river floods are often caused by very extreme precipitation. Flood protection rely on reliable estimates of the return values. Observational time series are too short for a precise calculation. Here, 100-year return values of daily precipitation are estimated on a global grid based on a large set of model-generated precipitation events from ensemble weather prediction. The statistical uncertainties of the return values can be substantially reduced compared to observational estimates.