EGU General Assembly 2020
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the Creative Commons Attribution 4.0 License.

Spatial variability of precipitation extremes over Italy using a fine-resolution gridded product

Benedetta Moccia1, Simon Michael Papalexiou2,3, Fabio Russo1, and Francesco Napolitano1
Benedetta Moccia et al.
  • 1DICEA Dipartimento di Ingegneria Civile, Edile e Ambientale, Università degli studi di Roma La Sapienza, Rome, Italy
  • 2Department of Civil, Geological and Environmental Engineering, University of Saskatchewan, Saskatoon, Canada
  • 3Global Institute for Water Security

Analysis of extreme precipitation events has been the cornerstone of statistical hydrology and plays a crucial role in planning and designing hydraulic structures. Extreme value theory offers a solid theoretical basis for using the Generalized Extreme Value (GEV) distribution as a probabilistic model to describe precipitation annual maxima. Several large-scale studies investigate the properties of the GEV distribution in point measurements offering insights on its spatial variability. Yet the sparse station network in most regions, as anticipated, leads to sparse point estimates that may distort the actual spatial patterns of the GEV’s parameters. Here, we use fine-resolution satellite-based gridded product, that is, the CHIRPS v2.0, to investigate the spatial variation of the GEV distribution over Italy. Our results show that the GEV shape parameter forms clear spatial patterns. We use these results to offer robust estimates and create maps for different return periods all over Italy.

How to cite: Moccia, B., Papalexiou, S. M., Russo, F., and Napolitano, F.: Spatial variability of precipitation extremes over Italy using a fine-resolution gridded product, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11948,, 2020

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Display material version 1 – uploaded on 02 May 2020
  • CC1: Spatial performance of GEV/Gumbel, Mohanad Zaghloul, 06 May 2020

    Hi Benedetta, thank you for your nice and clear presentation, I enjoyed reading it :)
    I have a quick question about assessing the performance of the GEV/Gumbel distributions compared to the precipitation sample. Is there any regional variation in the performance of the models? I think the approximation of the GEV to Gumbel (GEVshape < 0  to GEVshape ==0) will result in a spatial variation in the fitting performance.