EGU General Assembly 2020
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Modelling sub-hourly rainfall extremes with short records - a comparison of MEV, Simplified MEV and point process methods

Li-Pen Wang1,3, Francesco Marra2,4, and Christian Onof1
Li-Pen Wang et al.
  • 1Imperial College London, Civil and Environmental Engineering, London, United Kingdom of Great Britain and Northern Ireland (
  • 2The Fredy & Nadine Herrmann Institute of Earth Sciences, The Hebrew University, Jerusalem, 9190401, Israel
  • 3Department of Civil Engineering, National Taiwan University, Taipei, 10067, Taiwan
  • 4Institute of Atmospheric Sciences and Climate, National Research Council of Italy, Bologna, 49129, Italy

Accurate information on extreme rainfall frequency at sub-hourly timescales is useful for many hydrological applications, such as urban drainage design and stormwater management. However, the availability of sub-hourly rainfall records with sufficient length and quality is generally limited in most countries. With these short datasets, the conventional rainfall frequency analysis methods (e.g. annual maxima (AM) series) are prone to systematic biases and large uncertainties. In this work, we take advantage of long sub-hourly rainfall archives to explore the potential of alternative methods that exploit a larger fraction of the available data (or features), thus promising accurate estimates from relatively short data records.

The first method is based upon the Metastatistical Extreme Value (MEV) framework, which relaxes the asymptotic assumption of traditional AM methods. MEV considers, year by year, the full distribution of the underlying ordinary events and their number of occurrences. The second method, the Simplified MEV (SMEV, a variant of MEV), in which inter-annual variability is neglected in favour of simpler parametrisation and more robust parameter estimation, is also tested. So far, these two methods were shown to outperform traditional methods for daily amounts, but were never used on sub-hourly data.

The third method is based upon point process theory, which represents the temporal rainfall process in a realistic yet simple way, such that the hierarchical structure of rainfall is explicitly incorporated, and several parameters have a physical interpretation. Models based upon point process theory were known to be incapable of preserving extreme rainfall statistics at hourly and sub‑hourly timescales. Nonetheless, a recent breakthrough has overcome this deficiency (Onof and Wang, 2019). In this work, a revised randomised Bartlett-Lewis rectangular pulse model (RBL) is employed.

Five-minute rainfall data from 5 long recording rain gauges in Germany – Bochum (69 years), Aplerbeck, Kruckel, Marten and Nettebach (49 years) – are used. The comparison is conducted by resembling the scenarios where sub-hourly rainfall time series data are available with various short lengths (i.e. 5/10/15/20 years). SMEV and RBL generally outperform the MEV and AM in preserving sub-hourly rainfall extremes and are both much less sensitive to the use of short data records. SMEV outperforms RBL in preserving rainfall extremes at short return periods (< 10-year return periods), while they perform similarly at long return periods. RBL however has the advantage of preserving rainfall extremes across multiple timescales (i.e. from sub-hourly, hourly to 1-day) at the same time. The unsatisfactory performance of MEV is related to the influence of the low-intensity tail of yearly distributions.

How to cite: Wang, L.-P., Marra, F., and Onof, C.: Modelling sub-hourly rainfall extremes with short records - a comparison of MEV, Simplified MEV and point process methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6061,, 2020


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displays version 1 – uploaded on 04 May 2020
  • AC1: Comment on EGU2020-6061, Li-Pen Wang, 04 May 2020

    An experiment was undertaken to compare MEV, SMEV and RBL methods and the traditional AM timeseries fitted with GEV in preserving sub-hourly rainfall extremes with short records. The results suggest that SMEV and RBL largely outperfom MEV and GEV methods, and are much less sensitive to data length. The findings show the potential to be applied to the modelling of extremal behaviour of remote-sensing records, whose data lengths are generally short.