Contributions of natural climate changes and human activities to the trend of extreme precipitation
Introduction
Extreme precipitation leads to serious disasters that cause large human and economic losses because it tends to trigger natural hazards such as floods, landslides, and debris flows (Easterling et al., 2000). The global increase in the frequency and intensity of extreme precipitation events has been observed in numerous studies (e.g., Alexander et al., 2006; Coumou and Rahmstorf, 2012; Ingram, 2016; IPCC, 2013). Many previous studies showed that China experiences a significant increase in the extreme precipitation, in particular northwestern and southeastern China, during the rainy season from April to September (e.g., Fu et al., 2013; Ning and Qian, 2009; Wang and Zhou, 2005; Xu et al., 2011; You et al., 2011; Zhai et al., 2005; Zhang et al., 2009, Zhang et al., 2008).
The reasons behind the extreme precipitation changes have been widely discussed in the past decades. The IPCC (2013) believes that extreme precipitation events can inevitably be attributed to the combined influence of natural and human factors. Specifically speaking, global warming and anthropogenic aerosols are considered to be the major components affecting extreme precipitation changes (e.g., Liu et al., 2015; Qin et al., 2005; Rosenfeld et al., 2008; Wang, 2015). Ohba et al. (2015) concluded that extreme precipitation is significantly influenced by multiple effects of the East Asian monsoon, the El Niño/Southern Oscillation (ENSO), and the Pacific Ocean circulation in East Asia. Anomalies of the sea surface temperature of the tropical Pacific and India oceans were found to affect the precipitation intensity and typhoon paths in East Asia (Du et al., 2011; Ohba, 2013; Xie et al., 2009). The ENSO, Pacific Decadal Oscillation (PDO), and Southern Oscillation (SO) are found to be significantly associated with extreme precipitation in most regions of China, especially in southern China (e.g., Chan and Zhou, 2005; Wan et al., 2013).
Many studies pointed out that anthropogenic (human activity impacts) contribution causes more intense extreme precipitation events (e.g., Fischer and Knutti, 2015; Min et al., 2011). The effect of human activity on climate change is mainly reflected by fossil fuels, greenhouse gases from agricultural and industrial activities, land use and land cover change, and aerosol emissions (Ding, 2008). Gao et al. (2002) showed that the precipitation amount and intensity significantly increased in the Yangtze River Basin and eastern China at a double CO2 condition based on a nested Regional Climate Model. Land use changes affect the global and regional climate by changing the atmospheric moisture and energy balance of the ecosystem (Fu, 2003; Liu et al., 2011; Salazar et al., 2015).
However, particular contributions are still debated. It is known that precipitation and large-scale circulations, anthropogenic aerosols, and synoptic processes have a complex relationship, in particular on regional scales (Ohba et al., 2015; Wang, 2015). Under these conditions, meteorological and hydrological series show significant nonstationarity characteristics. Thus, the conventional stationary hypothesis is questionable to assess flood risk as well as to design hydraulic engineering projects in a changing environment (Beguería et al., 2011; Gilroy and McCuen, 2012; Milly et al., 2008; Zhang et al., 2014). The changes of the mean (Mn) and variance (Var) of the data series primarily show nonstationarity (Khaliq et al., 2006). The remarkable change in the intensity and frequency of an extreme precipitation event could be due to a small change in the mean (Mearns et al., 1984). The effect of the variance is greater than the average state of the climate, which suggests that an increased variance possibly leads to more extreme precipitation, even when the climate is in an invariable mean state (Katz and Brown, 1992).
The southeastern coastal region of China, including the Shanghai City, Zhejiang, Fujian, Guangdong, and Hainan provinces (excluding the South China Sea Islands), is located in a typical subtropical monsoon climate zone. It is an economically developed area with a high population density and a high concentration of wealth (Fig. 1). However, natural disasters, such as floods, typhoons, and mountain torrents, often occur in this region. Based on preliminary statistics, >182 extreme precipitation events occurred in the Fujian Province alone, which caused >2000 deaths from 1950 to 2000 (Song and Cai, 2007).
The investigation of the variation of extreme precipitation and its possible impact factors is therefore of great importance and helpful for the risk assessment of natural disasters and water resource management in the southeastern coastal region of China. This study therefore focuses on the extreme precipitation in the first flood season (April to June). The objectives include: 1) detecting the nonstationarity characteristics of extreme precipitation by analyzing the trends of the mean and variance of the maximum daily precipitation series (MDP) and 2) investigating the contributions of natural climate change and human activities to extreme precipitation based on a nonstationarity model, the Generalized Additive Models for Location, Scale, and Shape parameters (GAMLSS). Six large-scale circulation indices and emissions of four greenhouse gases were selected to represent natural climate change (non-anthropogenic) and human activity effects (anthropogenic), respectively. The remainder of this paper is structured as follows. Section 2 describes the datasets. The analysis methods are presented in Section 3, and the results are presented in Section 4. Finally, the discussion and conclusions are presented in Section 5.
Section snippets
Meteorological observations
Seventy-nine meteorological stations conducting long-term consecutive measurements (no gaps exceeding two consecutive weeks) in the southeastern coastal region of China were selected from the China Meteorological Data Sharing Service System of the National Meteorological Information Center (http://data.cma.cn/; Fig. 1, Table 1). The daily precipitation records in the first flood season (April to June) during 1960 to 2012 were extracted for the analysis. An extreme precipitation series
GAMLSS model
To simulate the linear and nonlinear relations between response and explanatory variables under nonstationarity conditions, the Generalized Additive Models for Location, Scale, and Shape parameters (GAMLSS) was developed by Rigby and Stasinopoulos (2005). The ability of introducing multiple explanatory variables is the outstanding advantage of GAMLSS. Previous studies have shown that GAMLSS was suitable for the trend analysis of precipitation, temperature, and runoff series (e.g., L. Gao et
Trends of Mn and Var of MDP
Fig. 2 and Table 2 illustrate the MK trends for Mn and Var of MDF based on GAMLSS at different confidence levels from 1960 to 2012. In total, 15 stations have significant decreasing Mn trends in the study area. Thirteen stations show the most significant decreasing trend (99% confidence level); they are mainly located in the east of Guangdong, northern Fujian, and in the west of Hainan. However, two other stations show increasing trends. In total, 17 stations with remarkable increasing trends
Discussion and conclusions
Although the nonstationarity characteristics of extreme precipitation from April to June from 1960 to 2012 are not widespread throughout the southeastern coastal region of China, the trends are significant on local scales. Previous studies have shown similar results in this region. For example, Gao et al. (2016) found 48 of 631 stations show significant nonstationarity for the annual maximum daily precipitation (AMP) series. 25 of 48 stations showing significant positive trend mainly
Acknowledgments
This study was supported by National Natural Science Foundation of China [grant number 41501106], National Social Science Foundation of China [grant number 14ZDB151], Scientific Projects from the Fujian Provincial Department of Science & Technology [grant number 2015J05080], and Scientific Research Foundation for Returned Scholars, Ministry of Education of China [grant number 2014-1685]. The meteorological data have been provided by China Meteorological Data Sharing Service System of National
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