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 conditions 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 activities. 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 significantly 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 western extent of subtropical high over the However, natural disasters such as floods, typhoons, and western Pacific and the northern extent and the ridge waterloggings often happen in this region. In the first line of the subtropical high over the South China Sea decade of the twenty-first century, the average annual (1008–1208E), affect summer precipitation significantly disaster areas affected by typhoons and floods were (e.g., Wei and Huang 2010). Ho et al. (2004) noted that 1166.2 3 103 and 569.5 3 103 ha, respectively (Wang et al. the interdecadal variability of typhoon tracks is affected 2013). Therefore, it is of great importance to assess the

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large data gap in the record. We eliminated the missing data that did not affect our analysis. According to the mechanisms by which precipitation forms, the flood season is from April to September. The flood season can be further divided into two subphases: the first flood season from April to June and the second flood season from July to September. The extreme precipitation in the second flood season mainly results from tropical cyclone activities and severe convection weather (Liu et al. 2013). A total amount of daily precipitation exceeding the 99th percentile of daily precipitation series from July to September was retrieved for each year.

Thus, an extreme precipitation series (hereafter R99) was constructed for each station, which was used as a response variable in GAMLSS. It should be noted that we did not use a single maximum precipitation event for constructing the extreme precipitation series to avoid a very rare extreme maximum (i.e., outlier). Thus, a conventional percentage (99th) was adopted to define the extreme precipitation amount (e.g., Zhang et al. 2008). Although, R is possibly retrieved from one precipitation FIG. 1. Location of meteorological stations and study area. 99 event for one year and two or more precipitation events for another year, it does not affect the fundamental trends risk changes of extreme precipitation in this region for of extreme precipitation in a long-term series. Mean (Mn) natural disaster reduction and management of water re- and variance (Var) are the most important indicators to sources under nonstationarity conditions. To achieve this reflect the stability and variability of a trend in a contin- major motivation of this study, the objectives include 1) uous data series. Therefore, the present study explored investigating whether nonstationarity is existent or not in the variations in R99 by investigating the Mn and Var this region and how it changes over time, 2) analyzing the based on the Mann–Kendall (MK) trend test. contribution of climate change and human activity to ex- Although, global climate change and human activity treme precipitation, and 3) investigating the recurrence are interactional, in this study, we simply selected 10 risk of extreme precipitation. This study provides a scien- large-scale circulation indices to represent natural cli- tific basis for flood risk analysis, water resource manage- mate change (nonanthropogenic) and emissions of four ment, and design of hydraulic projects in the southeastern greenhouse gases to represent human activity effects coastal region of China. The remainder of this paper is (anthropogenic) according to previous studies. The structured as follows. Section 2 describes the meteoro- natural climate change factors include four frequently logical observations as well as the analysis methods. The used circulation indices and six multivariate ENSO in- results are presented in section 3, and finally, the discussion dices (MEIs): the ridge line of the subtropical high over and conclusions are presented in section 4. the western Pacific (1108–1508E; RLWP), index of the western extent of subtropical high over the western 2. Data and methodology Pacific (WEWP), index of the northern extent of the subtropical high over the South China Sea (1008–1208E; a. Datasets NESCS), the ridge line of the subtropical high over the Daily precipitation records from 1960 to 2012 at 86 South China Sea (1008–1208E; RLSCS), the MEI in the meteorological stations in the southeastern coastal re- previous spring (PS), the MEI in the previous summer gion of China derived from the China Meteorological (PSu), the MEI in the previous autumn (PA), the MEI in Data Sharing Service System of the National Meteoro- the previous winter (PW), the MEI in the current spring logical Information Center (http://data.cma.cn/) were (CS), and the MEI in the current summer (CSu). RLWP, used in the analysis (Fig. 1, Table 1). The selection cri- WEWP, NESCS, and RLSCS data were derived from teria for stations were long-term consecutive measure- the Climate Diagnostics and Prediction Division of the ment with no gaps exceeding two consecutive weeks. National Climate Center of China (http://ncc.cma.gov. The quality of the observations was strictly tested by the cn). The MEI data were derived from the Earth System provider. It is worth noting that very few stations have a Research Laboratory Physical Sciences Division at the

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TABLE 1. Information on meteorological stations.

No. Station Lat (8N) Lon (8E) No. Station Lat (8N) Lon (8E) 1 Baoshan 31.42 121.47 44 Lianxian 24.78 112.38 2 Huzhou 30.88 120.12 45 Shaoguan 24.92 113.12 3 Hangzhou 30.33 120.23 46 Fogang 23.87 113.53 4 Pinghu 30.63 121.10 47 Yingde 24.17 113.40 5 Cixi 30.27 121.17 48 Lianping 24.22 114.29 6 Shengsi 30.73 122.45 49 Xinfeng 24.05 114.20 7 Chun’an 29.62 119.02 50 Longchuan 24.10 115.25 8 Jinhua 29.17 119.75 51 Dapu 24.35 116.70 9 Shengxian 29.60 120.82 52 Meixian 24.13 116.25 10 Yiwu 29.32 120.07 53 Zhangzhou 24.53 117.68 11 Yinxian 29.87 121.50 54 Chongwu 24.88 118.90 12 Quzhou 28.97 118.88 55 Xiamen 24.45 118.07 13 Lishui 28.47 119.92 56 Guangning 23.63 112.43 14 Xianju 28.87 120.73 57 Zhaoqing 23.05 112.45 15 Hongjia 28.67 121.50 58 Qingyuan 23.60 113.07 16 Dachendao 28.45 121.88 59 Guangzhou 23.00 113.22 17 Yuhuan 28.08 121.28 60 Dongyuan 23.73 114.68 18 Shaowu 27.33 117.47 61 Zengcheng 23.30 113.82 19 Wuyishan 27.72 118.00 62 Huiyang 23.37 114.18 20 Pucheng 28.00 118.58 63 Wuhua 23.93 115.77 21 Jianyang 27.33 118.10 64 Zijin 23.63 115.18 22 Jian’ou 27.08 118.32 65 Shantou 23.33 116.67 23 Yunhe 27.97 119.62 66 Huilai 23.03 116.30 24 Shouning 27.53 119.42 67 Dongshan 23.75 117.52 25 Ruian 27.48 120.37 68 Nan’ao 23.43 117.03 26 Fuding 27.20 120.13 69 Xinyi 22.37 110.93 27 Ninghua 26.23 116.63 70 Luoding 22.77 111.57 28 Taining 26.88 117.13 71 Taishan 22.27 112.77 29 Nanping 26.65 118.17 72 Zhongshan 22.40 113.35 30 Youxi 26.17 118.15 73 Shenzhen 22.55 114.12 31 Xiapu 26.88 120.00 74 Shanwei 22.77 115.37 32 Ningde 26.40 119.31 75 Zhanjiang 21.03 110.47 33 Fuzhou 26.08 119.30 76 Yangjiang 21.90 111.95 34 Changting 25.45 116.20 77 Dianbai 21.50 111.00 35 Shanghang 25.05 116.42 78 Shangchuandao 21.68 112.80 36 Yong’an 25.97 117.35 79 Xuwen 20.33 110.17 37 Zhangping 25.18 117.24 80 Haikou 20.00 110.42 38 Longyan 25.10 117.10 81 Dongfang 19.13 108.63 39 Jiuxianshan 25.72 118.10 82 Danzhou 19.31 109.32 40 Pingnan 25.92 118.98 83 Qiongzhong 19.10 109.88 41 Xianyou 25.35 118.68 84 Qionghai 19.42 110.47 42 Pingtan 25.50 119.78 85 Sanya 18.23 109.48 43 Nanxiong 25.13 114.32 86 Lingshui 18.50 110.03

National Oceanic and Atmospheric Administration Because of the complexity of climate change and the (http://www.esrl.noaa.gov/psd/enso/mei/index.html). diversity of greenhouse gases, an integrated index of

Annual emissions of four greenhouse gases (CO2,CH4, climate change (CI) and human activity (HI) was con- SO2,andN2O) were selected to represent the contribution structed based on principal component analysis (PCA). of human activity to extreme precipitation. The emission Here, the Pearson correlation was simply used to eval- data were derived from the Emission Database for Global uate the connection between R99 and 10 climate change Atmospheric Research, version 4.2 (EDGAR v4.2), at the factors as well as CI (Table 2). However, it is notable Joint Research Center of the European Commission (http:// that Pearson correlation is not appropriate if the cor- edgar.jrc.ec.europa.eu/overview.php?v542).Thedataat relation is nonlinear. It is found that CI has a better 0.1830.18 were adjusted for individual stations according to correlation (65 stations at 90% conﬁdence level) than their coordinates. The small-scale mismatch was ignored, any single climate change factor. Thus, using the in- which did not affect the trendanalysisinthisstudy. tegrated index not only reﬂects the comprehensive

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TABLE 2. Correlation between R99 and 10 climate change factors as represents the joint function (here the cubic spline well as CI. function is applied) between distribution parameters Abbreviation Station statistic of and explanatory variables xjk. Forty-eight different lin- of climate correlation at 90% ear and nonlinear (i.e., stationarity and nonstationarity) change factor Range of correlation confidence level relationships were considered between response vari- RLWP From 20.2 to 0.43 15 able (R99) and explanatory variables (time, CI and HI) WEWP From 20.3 to 0.16 7 in this study. For example, the nonstationary model as a NESCS From 20.23 to 0.42 9 function of R and CI/HI varies with time, or a function 2 99 RLSCS From 0.3 to 0.37 11 of R and combined CI and HI varies with time. All PS From 20.36 to 0.32 11 99 PSu From 20.36 to 0.33 8 these functions were evaluated based on eight widely PA From 20.31 to 0.31 8 used distribution functions: gamma, lognormal, gener- PW From 20.39 to 0.33 13 alized gamma, inverse gamma, inverse Gaussian, re- CS From 22.1 to 0.41 7 verse Gumbel, Weibull (mu as mean), and skew normal 2 CSu From 4 to 0.38 11 type 2 (Table 3). The optimal distribution with the CI From 0.07 to 0.51 65 highest goodness-of-fit performance with the minimum Akaike information criterion (AIC) value was selected. impact of multiple climate change factors on pre- The parameters include mean and variance; skewness, cipitation, but also reduces the dimensions of variables kurtosis coefficient, and Filliben coefficient were also and improves the efficiency of GAMLSS. To account for evaluated by GAMLSS. Based on the selected distri- the combined effects of climatic factors fully, all of the bution function and modeled parameters, the extreme main components were extracted. The CI for each sta- precipitation amount at different return periods (per- tion can be calculated as follows: centages) could be predicted. We want to emphasize how to determine the contribution of explanatory vari- n ables to response variable via modeled parameter co- 5 å 3 CI (aixiri) Zi , (1) efficients. Normally, location parameter in a distribution i51 function is related to the order of magnitude of data, where CI is the integrated index of climate change, ai is whereas scale parameter is correlated to the variability the weight coefficient of each climate factor, xi is the of data. Thus, the fitting coefficient containing location standardized value of each climate factor, ri is the linear and scale parameter information is suitable for assessing correlation coefficient between climate change factor the impact of CI and HI on the trend changes of Mn and and R99, Zi is the contribution rate of principal compo- Var. Thus, in this study, CI and HI were applied as ex- nent, and n is the number of components. Similarly, the planatory variables in GAMLSS to fit R99 for each sta- HI can be calculated for each station. tion, and the recurrence risks of extreme precipitation based on 2- and 100-yr return periods were analyzed for b. GAMLSS model the southeastern coastal region of China quantitatively. GAMLSS can explain the linear, nonlinear, para- metric, and nonparametric relationship between response variables and explanatory variables via 3. Results distribution fitting. An advantage of GAMLSS is that it a. Trends of Mn and Var of R99 is capable of introducing multiple explanatory variables (Gilroy and McCuen 2012). Suppose n number of in- Before applying GAMLSS model, the linear re- dependent observations yi,theR99 in this study, which gression and MK test were used to detect the trend of is subject to the distribution of Fy(Yi j ui), where, R99 for individual stations without considering climate uiT 5 (ui1, ui2, ..., uip) represents p parameters (position, change and human activity factors. Generally, both scale, and shape) vector (p is normally less than four). methods did not find a significant trend in this region. Thus, a semiparametric GAMLSS is defined as follows: The coefficient of determination R2 in the linear re- gression method averaged 0.02 over 86 stations, and only m u 5 u b 1 å six stations passed the significance test at 95% confi- gk( k) k k hjk(xjk), (2) 2 j51 dence level. The maximum R is only 0.16. In the MK trend test, only eight stations have significant trends at u u where k is a vector of length n, k is the explanatory the 95% confidence level. Abrupt points could be found variable (i.e., the time, CI and HI) in the n 3 m matrix, around the mid-1980s for these stations. Other stations b k is a parameter vector of length m, and hjk( ) did not show significant trends and changepoints. This

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TABLE 3. Information on distribution functions. significant increasing Mn. The relatively lower confidence level (95%) trend is found in the middle of Zhejiang and Distribution function Abbreviation No. of parameters the eastern part of Guangdong. Gamma GA 2 For Var, five stations in Zhejiang, two stations in Lognormal LOGNO 2 Generalized gamma GG 2 Fujian, four stations in Guangdong, and one station in Inverse gamma IGA 2 Hainan show a significant decreasing trend at 99% Inverse Gaussian IG 2 confidence level. Only two stations show the decreasing Reverse Gumbel RG 2 trend at 95% confidence level, and they are located in Weibull (mu as mean) WEI3 2 Zhejiang and Guangdong, respectively. The Shanghai Skew normal type 2 SN2 3 station (station 1) also shows a decreasing trend at 90% confidence level. Twelve stations have significant in- indicates that conventional methods such as linear re- creasing trends of Var and are mainly located in gression are not always suitable to detect the non- Guangdong and Fujian. Only one station in Zhejiang stationarity of extreme precipitation series. (station 23) and one station in Hainan (station 83) show Figure 2 and Table 4 illustrate MK trends for Mn and the increasing trend. In general, Shanghai, Zhejiang, and Fujian have a stable trend increment in Mn, and the Var of R99 at different confidence levels from 1960 to 2012 using GAMLSS. In total, 10 stations show signifi- variability of R99 is not significantly enhanced. Although cant decreasing trends for Mn in the study area. Five the trend changes of Mn in Guangdong are not signifi- stations have the most significant decreasing trend (99% cant, the interannual variability of R99 is significantly confidence level) and are located in Zhejiang (station 2), strengthened in the middle and eastern regions because Guangdong (stations 57, 58, and 66), and Hainan (sta- increasing and decreasing trends occurred simulta- tion 84). However, for Mn, the increasing trend is much neously. The enhanced variability tends to cause more more significant than the decreasing trend. In total, 18 frequent extreme precipitation events. stations have remarkable increasing trends, implying b. Attribution analysis based on GAMLSS that extreme precipitation amounts increased from 1960 to 2012. Among them, eight stations are located in Fujian, According to AIC value, a certain distribution with two are in Zhejiang, and one each are in Shanghai, the highest goodness-of-fit performance was selected for Guangdong, and Hainan, all of which show the most each station (see Table 3 for distribution function names significant increasing trend (99%) for Mn. Fujian has the and abbreviations). IGA was selected at 24 stations, and most stations with significant increasing trends. Stations GA performed best at 13 stations. IG was selected at the 18, 22, and 28 are located in complex terrain (Wuyi most stations (31). LOGNO and RG were selected at 7 Mountains) and are much more vulnerable to extreme and 6 stations, respectively. GG, SN2, and WEI3 were precipitation. The southeastern coast of Fujian (Xiamen selected at less than 3 stations. There are two stations and Zhangzhou City) is also a high-risk area with [Yingde (station 47) and Yangjiang (station 76)] that did

FIG. 2. Spatial distribution of MK trends for (a) Mn and (b) Var of R99 from 1960 to 2012.

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TABLE 4. Station statistics of MK trends for Mn and Var of R99 positive impact on the trend of Mn and Var at more sta- from 1960 to 2012. tions than it has a negative impact. This finding also dem- Mn Var onstrates that CI has much more influence on Mn than Var. Figure 4 shows the spatial distribution of the fitting Confidence level 99% 95% 90% 99% 95% 90% coefficients for HI in GAMLSS. Twenty-six stations Decreasing trend 5 3 2 12 2 1 have a positive correlation between HI and Mn, which Increasing trend 13 5 0 7 5 0 means that HI has an increasing impact on Mn. They are mainly located in the north and south of Zhejiang and not show any significant nonstationarity with respect to the east and south of Fujian. Only a small number of Mn and Var. This means that the trend is stationary and stations in Shanghai, Guangdong, and Hainan show the did not change with time. The residual moments and positive correlation. The negative impact could be found computed Filliben coefficients were given in Table 5, at stations located in the west of Zhejiang and in the which show the performance of the GAMLSS model in middle and eastern part of Guangdong. More stations this analysis. Overall, GAMLSS showed encouraging show the negative impacts than the positive between HI performances. Normally, the Filliben coefficients are and Var (Table 6). Only a few stations in southern higher than 0.95, indicating that the fittings of GAMLSS Zhejiang and Fujian as well as eastern Guangdong show meet the analysis requirement (Gu et al. 2016). The the positive influence. Although, HI has some impacts Filliben coefficient ranges from 0.976 to 0.997, with an on the trend changes of Var, it is not significant with CI. average value of 0.991, which indicates that the residuals Considering the MK test of Mn and Var of R99 in the of the GAMLSS model follow the normal distribution previous section (Fig. 2), the stations in Zhejiang and very well. The averaged mean and variance value of the Fujian possibly affected by human activities are always residuals are nearly 0.0 and 1.0, which indicates the in- the stations that have significant trend increments of significant deviations from normality. The fitting co- Mn. This finding implies that human activities play a efficient for location parameter and scale parameter is positive role on the stable increment of extreme pre- simulated using CI and HI as the explanatory variables cipitation amount in these areas. based on GAMLSS. Table 6 illustrates the station sta- c. Risk changes of extreme precipitation tistics of fitting coefficient for Mn and Var of R99 from 1960 to 2012. Figure 3 shows the spatial distribution of The precipitation amounts for 2- and 100-yr return the fitting coefficients for CI in GAMLSS. A positive periods are selected to represent the high- and low- fitting coefficient means a positive impact (increasing) of frequency extreme precipitation events, respectively. CI/HI on Mn/Var and vice versa. The positive fitting The extreme precipitation amounts for 2- and 100-yr coefficient in almost the entire region (79 stations) return periods modeled by GAMLSS are applied to shows a positive impact of CI on Mn. Four stations analyze the risk of extreme precipitation for each station have a negative fitting coefficient: Shanghai (station 1), during the second flood season in the southeastern Fujian (station 34), and Guangdong (stations 50 and 65). coastal region of China. The MK trend test was used to The correlation between CI and Var is positive in assess the risk trend (Fig. 5, Table 7). The extreme Shanghai, the east of Zhejiang, the west and south of precipitation amount for a 2-yr return period decreases Fujian, the eastern part of Guangdong, and the middle significantly from the coastal region to inland and from of Hainan. Stations showing the negative impact are south to north. The maximum precipitation (higher than 2 larger in proportion (18.6%) and are mainly located in 110 mm day 1) happens in Hainan and the southern the west of Guangdong and the northeast of Fujian. The coastal region of Guangdong. According to the defini- correlation is not significant between CI and Var in the tion of rainfall levels from the China Meteorological middle of Guangdong and in the middle of Fujian with Administration, the minimum precipitation reaches the 2 the fitting coefficient value of zero. Generally, CI has a rainstorm level (50 mm day 1). This indicates that the

TABLE 5. Residual moments for GAMLSS model and computed Filliben coefﬁcient.

Kurtosis Filliben AIC Mn Var Skewness coefﬁcient coefﬁcient Value range From 435.616 From 20.168 From 0.904 From 20.728 From 1.798 From 0.976 to 625.243 to 0.202 to 1.103 to 1.09 to 3.67 to 0.997 Median 520.263 0.001 1.019 20.061 2.395 0.991

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TABLE 6. Station statistics of the fitting coefficient for Mn and Var respectively. Although overall the stations with in- of R99 from 1960 to 2012. creasing trends for a 100-yr return period are fewer than Mn Var those with a 2-yr return period, the entire Fujian has a definite risk of increasing. The middle part of Guang- Fitting coefficient .0 ,0 .0 ,0 dong shows a similar trend. Whenever a high- or low- CI 79 4 35 16 frequency extreme precipitation event happens, the HI 26 14 12 16 possibility of a severe flood disaster significantly increases in western and northern Fujian because of the complex risk of storm is high for the entire region. Furthermore, terrain. Extreme precipitation is possible and would the risk of high-frequency extreme precipitation events mainly be the result of active tropical cyclones that occur significantly increases in the east of Hainan, west and from July to September, which often land in the south- south of Fujian, east of Zhejiang, and in Shanghai. In eastern coastal region of China (Liu et al. 2013). total, 19 stations show the increasing trend at different To more intuitively illustrate the recurrence risk of confidence levels. Furthermore, Fujian has the most extreme precipitation, six representative stations with stations with the trend at 99% confidence. Because of significant increasing and decreasing trends were se- the complex terrain in western Fujian, attention should lected: stations 28 and 67 located in Fujian and stations be paid to the flash floods and debris flows possibly 62, 78, 57, and 61 located in Guangdong (Table 8). The caused by rainstorms. The risk decreases mainly in the goodness-of-fit information showed that the GAMLSS middle of Guangdong, as well as in the north and south model performed very well. The Filliben coefficient of Zhejiang and in the east of Hainan. In total, 15 sta- ranged from 0.979 to 0.995. The optimal distributions tions totally show the decreasing trend. also were different according to AIC. Figure 6 illustrates The extreme precipitation amount for a 100-yr return the temporal variation of extreme precipitation amount period generally increases from the northwest to the for R99, 2-yr return period, and 100-yr return period. It southeast of the study area. The coastal region always is noticeable that stations 28, 67, 62, and 78 showed a has much more precipitation. The maximum value oc- significant increasing trend of extreme precipitation curs in the west of Hainan. The precipitation amounts amount for 2- and 100-yr return periods, especially for for the 100-yr return period increase remarkably in the the latter. The extreme precipitation amount of the 100-yr eastern coastal region of Zhejiang, the western moun- return period changed from around 120 mm in 1960 to tains of Fujian, the northern coast of Fujian, and the 480 mm in 2012. Stations 57 and 61 showed a significant eastern part of Guangdong. A significant decrease decreasing trend for the 2- and 100-yr return periods. is found in the central and north of Zhejiang and in The extreme precipitation amount drops around 50% the middle of Guangdong. The number of stations from 1960 to 2012. It is worth noting that Huang et al. with decreasing and increasing trends is 20 and 15, (2016) found a significant increasing trend at stations 57

FIG. 3. Spatial distribution of the ﬁtting coefﬁcients for CI for (a) Mn and (b) Var in GAMLSS.

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FIG. 4. Spatial distribution of the fitting coefficients for HI for (a) Mn and (b) Var in GAMLSS. and 61 in the first flood season from 1960 to 2012. This precipitation, taking the impact of both climate change implies that the causes of extreme precipitation are and human activity into account during the second flood different in these two subphases. The extreme pre- season in the southeastern coastal region of China. The cipitation event in the second flood season is more likely recurrence risk of an extreme precipitation event was to be related to typhoon activity. Although the extreme calculated based on GAMLSS under the impact of both precipitation amount decreases at these two stations, the climate change and human activity. The results provided a risk is still high from an annual perspective. scientific basis for water resource management and regional planning in this region. Although numerous studies argued the combined 4. Discussion and conclusions impact of climate change and human activity on extreme In a changing environment, nonstationarity charac- weather events, it is still challenging to identify the main teristics of precipitation become universal and distinct. driving force in different periods. Meanwhile, it is nec- However, previous studies ignored nonstationarity in essary to investigate the most relevant impact factors on the trend analysis for precipitation and runoff series. extreme values. It is also a big challenge because the The present study overcame this deficiency and ana- impact factors are different for different extreme values lyzed the nonstationarity characteristics of extreme (runoff and rainfall) in different regions. For example,

FIG. 5. Spatial distribution of extreme precipitation amount for a (a) high-frequency precipitation event (2-yr return period) and (b) low-frequency event (100-yr return period) as well as the MK trends.

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TABLE 7. Station statistics of MK trends of extreme precipitation The nonstationarity model GAMLSS is suitable to amount for 2- and 100-yr return periods from 1960 to 2012. analyze the trends of extreme series and the possible Decreasing trend Increasing trend seasons (Zhang et al. 2014, 2015). It further reveals the potential causal relationship between the variations of Confidence level 99% 95% 90% 99% 95% 90% extreme precipitation events and climate change/human 2-yr return period 8 5 2 16 1 2 activities under a changing environment. However, the 100-yr return period 13 5 2 12 3 0 reasons for extreme precipitation changes are compli- cated. For extreme series itself, a changepoint may have a in the same area, Huang et al. (2016) found that the great impact on the trend. Zhang et al. (2014) found that contribution of human activity to the trend of Var was the existence of the changepoints mainly caused the greater than large-scale circulations during the first flood nonstationarity of annual peak flood in the Pear River season (April–June). However, in this study, climate change basin, China. Thus, an abrupt change before/after the (large-sale circulations) contributed more to Mn and Var. changepoint may result in a significant increase or de- Liu et al. (2015) and Wang (2015) also have different crease in Mn and Var. Huang et al. (2016) concluded that standpoints about the primary cause for changes in pre- the contribution of human activity to extreme precipitation in eastern China. In this study, we only dis- cipitation in the first flood season during the rapid so- cussed the correlation between climate change/human cioeconomic development period (1986–2012) is greater activity and extreme precipitation qualitatively. However, than that in slow development period (1960–85) in the a quantitative assessment is necessary in the future. Re- southeastern coastal region of China. In other words, gardless, extreme precipitation is doubtlessly increasing Huang et al. (2016) determined that the year of 1985 was a globally (Coumou and Rahmstorf 2012; Ingram 2016). changepoint to distinguish the strength of human activi- Certainly, the variable selection differs for climate change ties. We also detected the trends and changepoints of Mn and human activity. For example, the large-scale circulation and Var using MK in this study. There are 26 stations that indices such as the western Pacific index, the NPO, the showed changepoints in Mn and Var, respectively. Some PDO, geopotential heights, wind fields, and SST were often stations have more than one possible changepoint. selected to represent the climate changes (Alexander et al. However, most of the changepoints are in the mid-1980s, 2009; Huang et al. 2016; Wang and Zhou 2005; You et al. especially around 1987. Because of the limited paper 2011; Zhang et al. 2008). Except for aerosol and greenhouse length, the detailed analysis of the changepoints was not a gases data, reservoir information was also used to represent focus in this study. It is definitely an interesting topic and human activities (Zhang et al. 2015). In this study, because it should be investigated in the future. of the difficulty of obtaining of high-resolution greenhouse In summary, the nonstationarity of extreme precipita- gases and aerosol data, we applied only four main green- tion (R99) from July to September from 1960 to 2012 was house gas emissions. In addition to climate elements and significant in the southeastern coastal region of China. The human activities above, terrain and underlying surface Mn and Var of R99 were simulated by GAMLSS and de- characteristics (such as urbanization, heat island, and land tected by MK. The results showed that the increasing trend use/land cover) possibly have a significant strength- of R99 was stable, especially in Fujian. The variability was ening or weakening impact onvariationsinextreme significantly strengthened in the eastern part of Guang- precipitation. Certainly, other factors such as water dong while weakened in Shanghai and Zhejiang. Western vapor transportation and tropical cyclone should be Fujian is a much more vulnerable area because of the considered for the possible interpretation of physical complex terrain. Guangdong has a higher risk of flood causes behind changes in extreme precipitation. Be- because the interannual variability of R99 is significantly cause of the limitation of observation data, this is the strengthened in the middle and eastern regions with the content in the ongoing research. coexisting increasing and decreasing trends.

TABLE 8. Goodness-of-ﬁt performance of GAMLSS model at six representative stations.

No. Station Optimal distribution AIC Mn Var Skewness Kurtosis coefﬁcient Filliben coefﬁcient 28 Taining IGA 464.712 20.002 1.025 0.163 2.264 0.992 67 Dongshan IG 544.426 0.007 1.024 20.271 2.000 0.985 62 Huiyang IGA 548.805 20.024 1.046 0.080 2.151 0.991 78 Shangchuandao IG 582.815 0.001 1.020 20.020 2.373 0.995 57 Zhaoqing RG 506.596 20.029 1.008 0.421 2.194 0.979 61 Zengcheng IGA 515.518 0.061 0.996 20.266 2.568 0.986

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FIG. 6. Temporal variation of extreme precipitation amount for R99, 2-yr return period, and 100-yr return period at six representative stations.

Based on the climate change (CI in this study) and the of rainfall levels from the China Meteorological Admin- human activity (HI in this study) indices modeled in istration. The highest value of the 100-yr return period in GAMLSS, the results indicated that climate variability the second flood season was in the west of Hainan. The and human activities had more impacts on Mn than Var precipitation amount in coastal areas was generally more of R99 in the second flood season. This finding differs than in inland. The areas where the precipitation increased from the conclusion by Huang et al. (2016) for the first significantly were mainly distributed in the northeastern flood season. The main possible reason for the differ- coast of Fujian and the inland northeast and the eastern ences is the different synoptic processes of precipitation part of Guangdong. The possibility of severe flood disaster in these two flood seasons. Climate variability affected could significantly increase in these areas. Overall, high Var significantly in Zhejiang, Fujian, the eastern part of risks of extreme precipitation events and rapid socio- Guangdong, and almost all of Hainan. Human activities economic development have coexisted for a long time had more influence on Var in Guangdong than in in the southeastern coastal region of China. Shanghai, Zhejiang, Fujian, and Hainan. To date, our analysis was limited to 10 climate change The precipitation amount of high-frequency (2-yr indices and four human activity indices for testing the trend return period) and low-frequency (100-yr return period) of extreme precipitation events under nonstationarity con- extreme precipitation events was modeled by GAMLSS. ditions. To better distinguish the impacts between climate The results indicated that extreme precipitation shows a change and human activity, different with previous studies significant decreasing trend from the coast to inland and (e.g., Villarini et al. 2010; Zhang et al. 2015), we did not from south to north. The precipitation increased re- perform the analysis directly using all variables but with the markably in Shanghai, the middle and east of Zhejiang, integrated CI and HI. However, this procedure may bring northwestern Fujian, and the southeastern coast of Fujian. some open issues. First, the CI and HI obtained from PCA However, regarding intensity of precipitation, the entire are based on statistical relationships rather than physical region has a high risk of storm according to the definition mechanisms. Because of the complexity of precipitation,

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