Nonstationary bayesian modeling of precipitation extremes in the Beijing-Tianjin-Hebei Region, China
Graphical abstract
Introduction
The variations in extreme precipitation have been receiving more and more attention as a part of the changing trends in the global climate (Alexander et al., 2006; Easterling et al., 2000). The intensity and frequency of extreme precipitation have been varying with positive or negative trends in different regions (Aerenson et al., 2018; Dong et al., 2011). Simultaneously, the spatiotemporal distribution of extreme precipitation varies between regions in terms of their magnitude, intensity, and temporal distribution within a given time frame (Donat et al., 2016). As a consequence, there is a need to fully investigate the observed and projected changes of regional extreme precipitation, which is crucial for policymakers developing regional prevention schemes for precipitation-related natural disasters (Li et al., 2018; Zhao et al., 2019).
Frequency analysis based on extreme value theory is widely used to establish a relationship between extreme values and return periods. Generally, under the assumption of stationary, the return period analysis provides crucial information that is used in a wide range of applications, such as urban and rural development, public infrastructure, watershed management (Cheng et al., 2014). Traditionally, it is assumed that the probabilistic characteristics of hydro-meteorological processes will not change over time, and that future water resources planning can be designed with records (Sarhadi et al., 2016). However, climate change and human activities can generate trends in hydro-meteorological series, which may become nonstationary. Milly et al. (2008) asserted that nonstationary probabilistic models should be identified and applied because anthropogenic climate change is affecting the extremes of hydrologic variables. Afterward, various studies on nonstationary modeling were conducted to predict future events under changing environmental conditions, taking into account time-varying and physical-based covariates, such as time, temperature and climate indices (Cheng and AghaKouchak, 2014; Lu et al., 2019; Mondal and Daniel, 2019; Ragno et al., 2019; Sun et al., 2017; Um et al., 2017; Xiong et al., 2018).
Recently, changes in total and extreme precipitation over China have been examined (Chen et al., 2019; Xiao et al., 2017; You et al., 2011; Zhai et al., 2005), as well as the nonstationary analysis of precipitation extremes (Gao et al., 2016; Gu et al., 2019). In China, the climate is strongly influenced by the eastern Asian monsoon (Ding and Chan, 2005). Certainly, it is also affected by large-scale atmospheric circulation, such as El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Indian Ocean Dipole (IOD). Considering that there are distinct discrepancies for each region in China with different climatic and geographical conditions, there is a need to fully investigate the changes in precipitation extremes and possible influence factors for different regions in China.
The Beijing-Tianjin-Hebei (BTH) region, as one of the metropolitan areas of China, suffers from various precipitation-related natural hazards (like extreme precipitation, urban flooding, flash flooding, and mudslide). Although previous studies have demonstrated that most of the precipitation extremes in this region have a decreasing trend during the past five decades (Mei et al., 2018; Song et al., 2019a; Zhao et al., 2019), few studies have focused on the frequency analysis of precipitation extremes under a changing environment, which may be important for the public safety and engineering design. Zhang et al. (2015a) investigated the nonstationarity of precipitation extremes over the BTH region with the climate indices as the external covariates based on 12 meteorological stations. However, only the use of climate indices as covariates may not be appropriate for the nonstationary modeling of precipitation extremes. To our best knowledge, the full understanding of the nonstationarity of precipitation extremes in BTH and their expected return levels under stationary and nonstationary conditions have not been conducted.
The objectives of this study are: (1) to know about the nonstationary behavior of precipitation extremes based on block maxima series; (2) to estimate the expected return levels of precipitation extremes under stationary and nonstationary conditions; (3) to determine the best covariates and models for frequency analysis of precipitation extremes; and (4) to quantify the uncertainties in return levels from stationary and nonstationary models. This study provides a protocol in terms of nonstationary precipitation behavior in regions of climatic demarcation between semi-arid and semi-humid climates with the decreasing trends in total precipitation and precipitation extremes. Besides, the results will be important for the understanding of precipitation extremes in a changing environment at a regional scale.
Section snippets
Study area
The BTH is located in northern China and includes Beijing Municipality, Tianjin Municipality, and other 11 cities in Hebei Province (Fig. 1), which covers an area of approximately 2.2 × 105 km2. The eastern and southern BTH is adjacent to the Yellow River and the Bohai Sea. Taihang and Yan mountains are in western and northern BTH, accounting for about 53.58% of the total area. BTH is dominated by a typical temperate continental monsoon climate between the mid-latitude coastal and inland
Methodology
In this study, the stationary and nonstationary modeling of precipitation extremes are investigated based on the Bayesian framework, and the proposed framework of frequency analysis is shown in Fig. 2.
Trend analysis of precipitation extremes
To examine whether the behavior of precipitation extremes over the BTH region has been influenced under a changing environment, the Mann-Kendall test, Sen's slope, and Pettitt test were applied to the historical data set for all the stations. As illustrated in Fig. 3a, about 82.18% of the sites exhibited a decreasing trend for the AMP, with nine of them showing a significant decrease. Similar results were found based on the Sen's slope, highlighted by the stars (Fig. 3b). The magnitude of the
Discussion and conclusions
This study aimed to determine whether the nonstationary models were required for modeling precipitation extremes with different AMP time series under a changing environment, to identify the best model for frequency analysis of precipitation extremes considering different criteria, and to estimate the uncertainties of expected return levels for precipitation extremes based on a Bayesian framework by performing a case study with the daily precipitation from the 101 meteorological stations across
Data statement
The daily precipitation is obtained from the National Meteorological Information Center of China Meteorological Administration (http://data.cma.cn/). Besides, the normalized EASMI is available at http://ljp.gcess.cn/dct/page/65577. The ENSO data are available at https://www.esrl.noaa.gov/psd/data/climateindices. The monthly WPI and NAO series can be download from the NOAA (https://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml). PDO can access at //research.jisao.washington.edu/pdo/PDO.latest.txt
Author statement
Conceptualization and methodology: X. Song, J. Zhang.
Model calculation: X. Song, X. Zou, Y. Mo, C. Zhang, Y. Tian.
Data pre-processing and visualization: X. Zou, Y. Mo, C. Zhang, Y. Tian.
Software and code revised: X. Song.
Formal analysis: X. Song, X. Zou, Y. Mo.
Funding acquisition: X. Song, J. Zhang.
Writing – original draft: X. Song.
Writing – review & edit: all the authors.
Declarations of Competing Interest
The authors declare that they have no conflict of interest.
Acknowledgments
This work was supported by the Fundamental Research Funds for the Central Universities (2015XKMS034), the National Key Research & Development Program of China (2017YFC1502701), the National Natural Science Foundation of China (51609242, 51979271), the China Postdoctoral Science Foundation (2018M632333). This project was also funded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions. The first author is grateful for support from the China Scholarship
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