Long-term variability of extreme precipitation with WRF model at a complex terrain River Basin

Yinchi Zhang; Chao Deng; Wanling Xu; Yao Zhuang; Lizhi Jiang; Caiying Jiang; Xiaojun Guan; Jianhui Wei; Miaomiao Ma; Ying Chen; Jian Peng; Lu Gao

doi:10.1038/s41598-024-84076-x

Long-term variability of extreme precipitation with WRF model at a complex terrain River Basin

Sci Rep. 2025 Jan 2;15(1):156. doi: 10.1038/s41598-024-84076-x.

Authors

Yinchi Zhang^{1

2

3}, Chao Deng^{1

2

3}, Wanling Xu⁴, Yao Zhuang^{5

6

7}, Lizhi Jiang⁶, Caiying Jiang⁸, Xiaojun Guan⁶, Jianhui Wei⁹, Miaomiao Ma¹⁰, Ying Chen^{1

2

3

11}, Jian Peng^{12

13}, Lu Gao^{14

15

16

17}

Affiliations

¹ Key Laboratory for Humid Subtropical Eco-geographical Processes of the Ministry of Education, Fujian Normal University, Fuzhou, 350117, China.
² Institute of Geography, Fujian Normal University, Fuzhou, 350117, China.
³ College of Geographical Science, Fujian Normal University, Fuzhou, 350117, China.
⁴ School of Ocean and Earth Science, Tongji University, Shanghai, 200092, China.
⁵ Fujian Meteorological Observatory, Fuzhou, 350001, China.
⁶ Fujian Key Laboratory of Severe Weather, Fuzhou, 350001, China.
⁷ Key Laboratory of Straits Severe Weather, China Meteorological Administration, Fuzhou, 350001, China.
⁸ Nanping meteorological bureau of Fujian Province, Nanping, 353000, China.
⁹ Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, 82439, Garmisch- Partenkirchen, Germany.
¹⁰ China Institute of Water Resources and Hydropower Research, Beijing, 100038, China.
¹¹ Fujian Provincial Engineering Research Center for Monitoring and Accessing Terrestrial Disasters, Fujian Normal University, Fuzhou, 350117, China.
¹² Department of Remote Sensing, Helmholtz Centre for Environmental Research-UFZ, Permoserstrasse 15, 04318, Leipzig, Germany.
¹³ Remote Sensing Centre for Earth System Research, Leipzig University, Talstr.35, 04103, Leipzig, Germany.
¹⁴ Key Laboratory for Humid Subtropical Eco-geographical Processes of the Ministry of Education, Fujian Normal University, Fuzhou, 350117, China. l.gao@foxmail.com.
¹⁵ Institute of Geography, Fujian Normal University, Fuzhou, 350117, China. l.gao@foxmail.com.
¹⁶ College of Geographical Science, Fujian Normal University, Fuzhou, 350117, China. l.gao@foxmail.com.
¹⁷ Fujian Provincial Engineering Research Center for Monitoring and Accessing Terrestrial Disasters, Fujian Normal University, Fuzhou, 350117, China. l.gao@foxmail.com.

Abstract

Global warming has profound effects on precipitation patterns, leading to more frequent and extreme precipitation events over the world. These changes pose significant challenges to the sustainable development of socio-economic and ecological environments. This study evaluated the performance of the new generation of the mesoscale Weather Research and Forecasting (WRF) model in simulating long-term extreme precipitation events over the Minjiang River Basin (MRB) of China from 1981 to 2020. We calculated 12 extreme precipitation indices from the WRF simulations and compared them with observations. The spatio-temporal variations of extreme precipitation were further analyzed in terms of intensity, frequency, and duration. The results indicated that the WRF model can appropriately reproduce the spatial distribution of extreme precipitation indices with acceptable biases. The performance is significantly better for intensity and frequency indices compared to duration indices. Except for PRCPTOT and R10mm, WRF accurately captures the interannual variations of extreme precipitation. Meanwhile, the results of the pre-whitening Mann-Kendall (PWMK) test suggested that WRF can identify significant increasing trends in extreme precipitation, particularly for R95p, R99p, and R50mm. This study provides valuable insights for extreme precipitation forecasting and warning in other mountainous regions.

Keywords: Dynamical downscaling; Extreme precipitation; Minjiang River Basin; Spatio-temporal variations; WRF.

Abstract

Grants and funding