Risk of Extreme Precipitation Under Nonstationarity Conditions During the Second Flood Season in the Southeastern Coastal Region of China

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Risk of Extreme Precipitation Under Nonstationarity Conditions During the Second Flood Season in the Southeastern Coastal Region of China MARCH 2017 G A O E T A L . 669 Risk of Extreme Precipitation under Nonstationarity Conditions during the Second Flood Season in the Southeastern Coastal Region of China LU GAO,JIE HUANG,XINGWEI CHEN,YING CHEN, AND MEIBING LIU Institute of Geography, and College of Geographical Science, and Fujian Provincial Engineering Research Center for Monitoring and Assessing Terrestrial Disasters, and State Key Laboratory of Subtropical Mountain Ecology, Fujian Normal University, Fuzhou, China (Manuscript received 20 May 2016, in final form 1 December 2016) ABSTRACT This study analyzes the variation and risk changes of extreme precipitation under nonstationarity condi- tions using the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) and the Mann– Kendall (MK) test. The extreme precipitation series is extracted from the observations during the second flood season (July–September) from 1960 to 2012 derived from 86 meteorological stations in the southeastern coastal region of China. The trend of mean (Mn) and variance (Var) of extreme precipitation is detected by MK. Ten large-scale circulation variables and four greenhouse gases are selected to construct a climate change index and a human activity index, which are based on principal component analysis. The recurrence risk of extreme precipitation is calculated by GAMLSS while considering climate changes and human ac- tivities. The results demonstrate that the nonstationarity characteristic of extreme precipitation is widespread in this region. A significant increasing trend of Mn is found in Shanghai, eastern Zhejiang, and northern and southern Fujian. An enhanced Var is found in eastern Guangdong. A significant positive correlation exists between climate changes/human activities and Mn/Var, especially in Zhejiang and Fujian. Generally, the contribution of climate changes and human activities to Mn is greater than it is to Var. In this region, the precipitation amount of high-frequency (2-yr return period) and low-frequency (100-yr return period) events increases from inland to coastal and from north to south. The government should pay careful attention to these trends because the intensity of extreme precipitation events and their secondary disasters could result in serious losses. 1. Introduction Zhai et al. 2005; Zhang et al. 2009, 2008). Therefore, quantitative assessment of extreme precipitation is of Extreme climate and weather events have attracted great importance for flood planning and hydraulic much attention in recent decades because they lead to project purposes (Milly et al. 2002). serious disasters that cause large human and economic A common measure of extreme events (e.g., runoff losses (Easterling et al. 2000). Extreme precipitation is and rainfall) is the r-year return period. However, most one of the most common extreme weather events, and it previous studies on the analysis of runoff and pre- tends to trigger associated natural hazards, such as cipitation extremes were based on the assumption of floods, urban waterloggings, landslides, and debris flows. stationarity. In a changing environment, the stationary The global increase of frequency and intensity of ex- hypothesis is no longer suitable to assess flood risk and treme precipitation events has been proven by numer- hydraulic engineering projects (Beguería et al. 2011; ous studies (e.g., Alexander et al. 2006; Coumou and Gilroy and McCuen 2012; Milly et al. 2008; Zhang et al. Rahmstorf 2012; Ingram 2016; IPCC 2013). Many pre- 2014). Thus, the nonstationarity should be taken into vious studies have shown that extreme precipitation is account when the frequency of flood or extreme weather increasing significantly in northwestern and southeast- events is analyzed. Precipitation has shown a wide- ern China, especially during the rainy season from April spread nonstationarity, primarily in the changes of mean to September (e.g., Fu et al. 2013; Ning and Qian 2009; and variance of data series (Khaliq et al. 2006). The Wang and Zhou 2005; Xu et al. 2011; You et al. 2011; recurrence risk of extreme precipitation events is sig- nificantly related to mean and variance of data series. A Corresponding author e-mail: Lu Gao, [email protected] tiny change in mean could result in a remarkable change DOI: 10.1175/JHM-D-16-0119.1 Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 09/27/21 10:38 AM UTC 670 JOURNAL OF HYDROMETEOROLOGY VOLUME 18 in intensity and frequency of an extreme precipitation by the westward expansion of the subtropical north- event (Mearns et al. 1984). Katz and Brown (1992) also western Pacific high. note that the effect of variance on the frequency of an Human activity is another important factor that af- extreme precipitation event is greater than the average fects extreme precipitation. Ding (2008) noted that the state of the climate. An increased variance could lead to effect of human activities on climate change is mainly more extreme precipitation events, even when the cli- the result of the combustion of fossil fuels, greenhouse mate is in an invariable mean state. gases from agricultural and industrial activities, land-use Many factors, such as climate change and human ac- and land-cover change, and aerosol emissions. Under an tivity, can result in the nonstationarity of extreme runoff increased CO2 concentration scenario, the precipitation and precipitation. IPCC (2013) concluded that the var- amount and intensity may significantly increase in the iability of extreme precipitation has been inevitably at- Yangtze River basin in winter and summer (Gao et al. tributed to the combined influence of natural and human 2002). A similar result was found in the frequency and factors. Global warming and anthropogenic aerosols are intensity of extreme precipitation in eastern China, the major components in climate systems that have been which increased when CO2 concentrations doubled widely discussed for their impact on extreme pre- (Zou et al. 2008). Land-use changes affect the global and cipitation changes (e.g., Liu et al. 2015; Qin et al. 2005; regional climate by changing the atmospheric moisture Rosenfeld et al. 2008; Wang 2015). However, the con- and energy balance of the ecosystem (Fu 2003; Liu et al. tributions of the possible causes for extreme pre- 2011; Salazar et al. 2015). cipitation are still being debated because of the complex However, the question remains how to detect the relationships between the large-scale circulations, the nonstationarity of extreme precipitation while taking effects of anthropogenic aerosols, and the synoptic climate change and human activity into account. An processes, in particular at the regional scale (Ohba et al. appropriate method is greatly needed. Ouarda and El- 2015; Wang 2015). Adlouni (2011) discussed nonstationarity models based A number of studies have been conducted to explore on Bayesian and generalized maximum likelihood esti- the reasons for extreme precipitation trends. For ex- mation methods. Gilroy and McCuen (2012) developed ample, Ohba et al. (2015) determined that the variability and applied a nonstationarity method that accounted for of extreme precipitation is significant on interannual and multiple nonstationarity factors. Rigby and Stasinopoulos interdecadal time scales under the multiple effects of the (2005) developed a nonstationarity analysis model called East Asian monsoon, El Niño–Southern Oscillation Generalized Additive Models for Location, Scale, and (ENSO), and Pacific Ocean circulation in the East Asia. Shape (GAMLSS) that could simulate the linear and Anomalies in sea surface temperature (SST) in the nonlinear relations between response and explanatory tropical Pacific and Indian Oceans strongly affect the variables under nonstationarity conditions. GAMLSS intensity of precipitation, even the paths of typhoons in has been used successfully in the trend analysis of pre- East Asia (Du et al. 2011; Ohba 2013; Xie et al. 2009). cipitation, temperature, and runoff series (Jiang and Chan and Zhou (2005) found that the early summer Xiong 2012; Villarini et al. 2010). Zhang et al. (2015) (May–June) monsoon rainfall is associated with both introduced the Arctic Oscillation (AO), North Pacific ENSO and the Pacific decadal oscillation (PDO) in Oscillation index (NPO), Pacific decadal oscillation in- southern China. Wan et al. (2013) also found that the dex (PDO), Southern Oscillation index (SOI), and res- record-breaking probability for extreme monthly pre- ervoir index into GAMLSS to explore the impact of cipitation and Southern Oscillation (SO) are signifi- climate change and human activity on the annual max- cantly associated in most regions of China. Tropical imum streamflow in the East River basin of China. cyclones often land in the southeastern coastal region of The southeastern coastal region of China, including China and bring abundant rainfall. Li and Zhou (2015) Shanghai City, Zhejiang, Fujian, Guangdong, and Hainan and Ying et al. (2011) found a positive trend in pre- Provinces (excluding the South China Sea Islands), is cipitation per tropical cyclone and a maximum 1-h pre- located in a typical subtropical monsoon climate zone. It cipitation over the south of the Yangtze River. It has is an economically developed area with a high density of been proven that some large-scale circulation indices, population and a high concentration of wealth (Fig. 1). such as the
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