Abstract
Quantifying the magnitude and frequency of extreme precipitation events is key in translating climate observations to planning and engineering design. Past efforts have mostly focused on the estimation of daily extremes using gauge observations. Recent development of high-resolution global precipitation products, now allow estimation of global extremes. This research aims to quantitatively characterize the spatiotemporal behavior of precipitation extremes, by calculating extreme precipitation return levels for multiple durations on the global domain using the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. Both classical and novel extreme value distributions are used to provide insight into the spatial patterns of precipitation extremes. Our results show that the traditional Generalized Extreme Value (GEV) distribution and Peak-Over-Threshold (POT) methods, which only use the largest events to estimate precipitation extremes, are not spatially coherent. The recently developed Metastatistical Extreme Value (MEV) distribution, that includes all precipitation events, leads to smoother spatial patterns of local extremes. For durations of 5 and 10 days, however, there are less events per year to fit the distribution (37 and 22 on average, respectively), leading to larger inter-annual variability and possible overestimation of the extremes. While the GEV and POT methods predict a consistent shift from heavy to thin tails with increasing duration, the MEV method predicts a relatively constant heaviness of the tail for any precipitation duration, opening up an important research question on what is the ‘correct’ tail behavior of extreme precipitation for different durations. The generated extreme precipitation return levels and corresponding parameters are provided as the Global Precipitation EXtremes (GPEX) dataset. These data can be useful for studying the underlying physical processes causing the spatiotemporal variations of the heaviness of extreme precipitation distributions.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 129558 |
| Journal | Journal of Hydrology |
| Volume | 621 |
| DOIs | |
| State | Published - May 5 2023 |
Bibliographical note
KAUST Repository Item: Exported on 2023-05-18Acknowledgements: We acknowledge the use of the mevpy Python package (https://github.com/EnricoZorzetto/mevpy) for the extreme value analysis. This work was carried out on the Dutch National e-Infrastructure (DNI) with support from the SURF cooperative. Enrico Zorzetto acknowledges support from the NASA Earth and Space Science Fellowship 80NSSC17K0364. Hylke Beck was supported in part by the U.S. Army Corps of Engineers’ International Center for Integrated Water Resources Management (ICIWaRM). Nick van de Giesen acknowledges support of the European Commission's Horizon 2020 Programme under grant agreement number 776691 (TWIGA). Ruud van der Ent acknowledges funding from the Netherlands Organization for Scientific Research (NWO), project number 016.Veni.181.015.
ASJC Scopus subject areas
- Water Science and Technology
