Author(s): Haochen Yan; Mingfu Guan

Linked Author(s): Haochen Yan

Keywords: Non-stationarity; Extreme rainfall; The Greater Bay Area; Frequency analysis; Multisource data merging; Time scales

Abstract: Sub-daily rainfall extremes are increasingly posing threats to the society in the warming climate. The low resolution and accuracy of gridded rainfall datasets and very limited accessibility/availability of gauge observations hinder a reliable characterization of such changing extremes. Taking the Greater Bay Area of China as an example, we developed a long-term (1991-2020), high spatiotemporal-resolution (10 km, hourly) rainfall dataset using a Random Forest-based multi-source merging technique. The dataset is demonstrated to outperform than all the candidate gridded products and effectively fill the gap among the sparse gauge networks. Furthermore, non-stationary frequency based on the dataset shows greater increases in rainfall intensities over the north-central part of the region compared with the southern coastal region. Our results show, for the first time, that urbanization nonlinearly increases rainfall intensities at different durations and return periods.

DOI: https://doi.org/10.3850/iahr-hic2483430201-212

Year: 2024