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Identification of the non-stationarity of extreme precipitation events and correlations with large-scale ocean-atmospheric circulation patterns: A case study in the Wei River Basin, China
Journal of Hydrology 548 (2017) 184–195
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Identification of the non-stationarity of extreme precipitation events and correlations with large-scale ocean-atmospheric circulation patterns: A case study in the Wei River Basin, China Saiyan Liu a, Shengzhi Huang a,⇑, Qiang Huang a, Yangyang Xie a, Guoyong Leng b, Jinkai Luan a, Xiaoyu Song a, Xiu Wei a, Xiangyang Li c a
State Key Laboratory Base of Eco-Hydraulic Engineering in Arid Area, Xi’an University of Technology, Xi’an 710048, China Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China c Northwest Engineering Corporation Limited, Xi’an 710065, China b
a r t i c l e
i n f o
Article history: Received 30 October 2016 Received in revised form 3 March 2017 Accepted 6 March 2017 Available online 8 March 2017 This manuscript was handled by Corrado Corradini, Editor-in-Chief, with the assistance of Philip Brunner, Associate Editor Keywords: Extreme precipitation Non-stationarity Heuristic segmentation method Change point Wei River Basin
a b s t r a c t The investigation of extreme precipitation events in terms of variation characteristics, stationarity, and their underlying causes is of great significance to better understand the regional response of the precipitation variability to global climate change. In this study, the Wei River Basin (WRB), a typical ecoenvironmentally vulnerable region of the Loess Plateau in China was selected as the study region. A set of precipitation indices was adopted to study the changing patterns of precipitation extremes and the stationarity of extreme precipitation events. Furthermore, the correlations between the Pacific Decadal Oscillation (PDO)/El Niño-Southern Oscillation (ENSO) events and precipitation extremes were explored using the cross wavelet technique. The results indicate that: (1) extreme precipitation events in the WRB are characterized by a significant decrease of consecutive wet days (CWD) at the 95% confidence level; (2) compared with annual precipitation, daily precipitation extremes are much more sensitive to changing environments, and the assumption of stationarity of extreme precipitation in the WRB is invalid, especially in the upstream, thereby introducing large uncertainty to the design and management of water conservancy engineering; (3) both PDO and ENSO events have a strong influence on precipitation extremes in the WRB. These findings highlight the importance of examining the validity of the stationarity assumption in extreme hydrological frequency analysis, which has great implications for the prediction of extreme hydrological events. Ó 2017 Elsevier B.V. All rights reserved.
1. Introduction There is consensus that global warming and the intensification of human activities (e.g., urbanization, irrigation, afforestation) tend to accelerate the global hydrological cycle, resulting in more frequent and severe hydroclimate extremes, such as floods, flashfloods, or droughts with devastating impacts on local settlements, agriculture, ecosystems, industry, and socio-economic development (Trenberth, 2011; Toreti and Desiato, 2008; Choi et al., 2009; Santos et al., 2011; Wang et al., 2012; Huang et al., 2014a; Leng et al. 2015a,b). The situation is even worse in developing countries because of the higher vulnerability to disasters due to the highly-dense population and imperfect drainage infrastructure
⇑ Corresponding author. E-mail address: [email protected] (S. Huang). http://dx.doi.org/10.1016/j.jhydrol.2017.03.012 0022-1694/Ó 2017 Elsevier B.V. All rights reserved.
(Croitoru et al., 2015). According to the fifth Intergovernmental Panel on Climate Change (IPCC) report, the intensity of extreme precipitation events will increase with global warming at a rate of approximately 7%°C1, much faster than the response of the mean precipitation (1–3%°C1; IPCC, 2013; Stocker et al., 2013). Therefore, an increasing number of studies emphasized the importance of investigating extreme precipitation events (Trenberth, 2011; Tabari et al., 2014; Madsen et al., 2014). On the global scale, Westra et al. (2014) reported an increase in the extreme rainfall intensity, with the magnitude of changes depending on the geographic location and duration of the events. Similarly, changing precipitation properties have also been studied in various regions of the world. Based on daily precipitation datasets and the Mann-Whitney U test, Verdon-Kidd and Kiem (2015) showed the statistically significant shift of the annual maximum daily rainfall at most stations across Australia. Besselaar et al.
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(2012) observed a general increase in extreme precipitation in Northern Europe in autumn, winter, and spring. A significant decrease of precipitation days, and increase of heavy precipitation events have also been confirmed in Romania (Croitoru et al., 2015). Limsakul and Singhruck (2016) claimed that the precipitation events became less frequent but more intense in Thailand. Based on various precipitation indices, Goswami et al. (2006) discovered a significant increase of extreme precipitation events both in frequency and magnitude and a significant decrease of the frequency of moderate precipitation events over central India during the monsoon seasons from 1951 to 2000. In China, both annual maximum daily precipitation and heavy precipitation days exhibit an increasing trend in the southern river basins, but a decrease in the northern river basins (Chen et al., 2011). In Canada, no evident trends in extreme precipitation, neither in frequency or intensity, have been found in the last century (Zhang et al., 2001). This means that the response of hydrological components to global climate change varies greatly on the regional scale. Hence, it is necessary to investigate precipitation variations from a regional standpoint. It was suggested that the design criteria of urban drainage infrastructures, which are based on the frequency-durationintensity curve in a certain period, should be revised because of the increasing intensity and frequency of precipitation extremes (Kiem and Verdon-Kidd, 2013; Wang et al., 2013). The traditional hydrological frequency analysis of extreme precipitation and floods is based on the assumption of stationarity (Kiem and Verdon-Kidd, 2013; Verdon-Kidd and Kiem, 2015). This means that historical features of extreme events can be used to derive extreme value statistics in the future (Madsen et al., 2014). However, changing environments (e.g., global warming and/or anthropogenic activities) might alter the statistical characteristics of hydroclimate time series (Verdon et al., 2004; Kiem and VerdonKidd, 2009), resulting in so-called non-stationarity (Jiang et al., 2015). Ignoring the stationarity would therefore lead to severe biases in the extreme event assessment (Wigley, 1985; Kiem and Verdon-Kidd, 2013; Jiang et al., 2015). Furthermore, hydrological modelling studies would suffer inevitable bias without accounting for the non-stationarity of hydroclimate data based on which hydrological model parameters are tuned. However, to date, relatively a few studies have addressed the stationarity of extreme precipitation events. Extreme precipitation is induced by the combined influences of climatic and non-climatic factors (e.g., global warming, land reclamation, land cover, ocean-atmospheric circulation patterns, and urbanization; Croitoru et al., 2015; Sun et al., 2015). Among these factors, the variation of large-scale ocean-atmospheric circulation patterns is recognized as one of the most important factors affecting the generation of precipitation (Verdon et al., 2004; Kiem and Verdon-Kidd, 2009; Kenyon and Hegerl, 2010; Ward et al., 2014; Croitoru et al., 2015; Verdon-Kidd and Kiem, 2015; Limsakul and Singhruck, 2016). For instance, Limsakul and Singhruck (2016) showed that El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) are the remote drivers of the extreme precipitation variability in Thailand. However, the methods adopted in previous studies to investigate the teleconnections between precipitation extremes and ocean-atmospheric circulation are commonly based on the correlation coefficient (e.g., Spearman’s rank order correlation), which cannot comprehensively reflect the changing relations (Huang et al., 2015). The Wei River Basin (WRB), the largest tributary of the Yellow River Basin (YRB), was selected as the study region. It is located in the middle reaches of the YRB and comprises a total drainage area of 135,000 km2. As the main grain-yielding and important industrial and commercial area in Northwest China, the basin is a major water supply source, which directly supports a popula-
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tion of 22 million people. With the establishment of the Guangzhong-Tianshui Economic Zone, it acts as a strong stimulus for the economic development of West China. The intensification of the hydrological cycle would further alter the spatial and temporal distribution of precipitation, thereby increasing the uncertainty of water supply in the basin (Huang et al., 2014b,c). Hence, an improved knowledge of extreme precipitation changes is required for effective water resources management. Moreover, the Loess Plateau in the northern WRB contains the most highly erosion-prone soil with fragile ecological conditions in the world. Extreme precipitation could give rise to the acceleration of soil erosion. Therefore, a comprehensive study of the precipitation extreme changes is of great significance for ecological restoration. Previous studies have investigated the precipitation trend and pattern in the WRB (Chang et al., 2014; Huang et al., 2014c). However, few studies have been conducted to identify the nonstationarity of extreme precipitation events and explore the associations with large-scale ocean-atmospheric circulation patterns in this basin. The main objectives of this study are: (1) to test if extreme precipitation events are still stationary under changing environments and (2) to explore if there are significant relationships between precipitation extremes and large-scale ocean-atmospheric circulation patterns. The primary novelty of this study is as follows: (1) Previous studies investigating the relations between the precipitation extremes and ocean-atmospheric circulation were commonly based on the correlation coefficient (e.g., Spearman’s rank order correlation), largely ignoring the changes or evolution of the relations. In this study, we adopt cross wavelet analysis, which can help us to comprehensively reveal the relationships in various time and frequency domains; (2) Furthermore, we choose several extreme indices covering the frequency, duration and intensity of extremes to investigate which specific aspects of extremes are more affected by ENSO or PDO. This has broad implications to better understand the relations between the precipitation extremes and ocean-atmospheric circulation. Although the WRB was selected as the study region, the methods adopted in this study can be applied to other regions. The remainder of the paper is organized as follows: Section 2 describes the study area and data collection. Section 3 focuses on the methods used in this study. The results and discussion are provided in Section 4, and the conclusions are presented in Section 5.
2. Study area and data 2.1. Study area The WRB (Fig.1) originates in the Gansu Province and cuts through the Shaanxi Province, extending over a latitude of 4° (between 33.5°N and 37.5°N) and longitude of 8° (between 103.5°E and 110.5°E). The Jinghe and Beiluohe rivers are the two largest tributaries of the basin, with drainage areas of 4.5 104 km2 and 2.69 104 km2, respectively. Located in a semi-dry and semi-humid monsoon climate zone, the basin is characterized by distinctive seasonality with abundant precipitation and high temperatures in summer, and sparse precipitation and low temperatures in winter. The annual precipitation over the basin is approximately 600 mm; 60% of precipitation falls in the flood season (from June to September). The mean annual temperature ranges from 7.8 °C to 13.5 °C (Chang et al., 2014). To investigate the long-term variation of precipitation extremes, the whole basin was divided into five sub-regions: upstream, midstream, downstream, Jinghe Region, and Beiluohe Region (Fig. 1).
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Fig. 1. The location of the meteorological and hydrological stations in the WRB and its five sub-basins.
2.2. Data The daily precipitation for 1960–2010 was obtained from the National Climate Center (NCC) of the China Meteorological Administration (CMA). A total of 21 meteorological stations were used, 15 of which are within the WRB and six (i.e., Lintao, Guyuan, Foping, Zhenan, Shangzhou and Yanan) are outside but close to the edges of the basin. The detailed information is listed in Table 1. The weather stations used in the study have a reasonable spatial coverage and include all types of topography and climatic regions in the WRB (Fig. 1 and Table 1). Therefore, regional features of the precipitation extreme variability in the WRB could be detected. Precipitation data were missing at six stations. However, the total missing data, which were replaced by the average values of the same day from neighboring stations, are less than 0.01%. Note that the length
of observed records might not be long enough to identify the change characteristics of extreme precipitation events of longer return periods (e.g., 1 in 100 years of events), which is out of the scope of this study. Nevertheless, these data are sufficient for the purpose of change detection (Partal, 2010; Nalley et al., 2012), which is the main aim of this study. To examine the relationships between the precipitation extremes and the large-scale ocean-atmospheric circulation patterns, the correlations between the extreme precipitation indices and ENSO/PDO indices were investigated. The monthly PDO index (PDOI) is defined as the leading principal component of the North Pacific monthly sea surface temperature variability, which is closely related to the variability of extreme precipitation (Limsakul and Singhruck, 2016; Voskresenskaya and Vyshkvarkova, 2016). Several indices are available for the representation of ENSO phe-
Table 1 The information on weather stations in the WRB.
a
NO.
Weather stationa
Time of establishment
Latitude (N)
Longitude (E)
Altitude (m)
Period
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Baoji Changwu Foping Guyuan Huajialing Huanxian Huashan Lintao Luochuan Minxian Pingliang Shangzhou Tianshui Tongchuan Wugong Wuqi Xi’an Xifengzhen Xiji Yanan Zhenan
1951.9 1956.9 1957.1 1956.1 1951.1 1957.1 1953.1 1951.1 1954.11 1951.1 1951.1 1953.3 1951.1 1955.1 1954.4 1956.1 1951.1 1951.1 1957.2 1951.1 1957.1
34.35 35.20 33.52 36.00 35.38 36.58 34.48 35.35 35.82 34.43 35.55 33.87 34.58 35.08 34.25 36.92 34.30 35.73 35.97 36.60 33.43
107.13 107.80 107.98 106.27 105.00 107.30 110.08 103.85 109.50 104.02 106.67 109.97 105.75 109.07 108.22 108.17 108.93 107.63 105.72 109.50 109.15
612 1206 827 1753 2450 1255 2064 1893 1159 2315 1346 742 1141 978 447 1331 397 1421 1916 958 693
1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010
Stations are ranged in alphabetical order.
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nomena (Kiem and Franks, 2001), such as the NINO l, NINO 3, NINO 3.4, Southern Oscillation Index (SOI; Ward et al., 2016), and Multivariate ENSO Index (MEI). In this paper, the monthly MEI time series was used for the investigation of the teleconnection between the extreme precipitation events and ENSO events. The MEI index was selected because it outperforms other indices by integrating more information than others (Kiem and Franks, 2001; Limsakul and Singhruck, 2016). Here, the monthly PDO data were obtained from http://research.jisao.washington.edu/ pdo/, while the monthly MEI were downloaded from http:// www.esrl.noaa.gov/psd/enso/mei/table.html. 3. Methodology 3.1. Precipitation indices In this study, eight extreme precipitation indices were defined, which can be classified into intensity, frequency, and duration indices. Detailed descriptions about the extreme precipitation indices can be found in Table 2. Specifically, the maximum 1-day and 3day precipitation amount (RX1day and RX3days), very wet day precipitation amount (R90), and extremely wet day precipitation amount (R95) are the indices to characterize the intensity of extreme precipitation events, whereas the frequency of very wet day precipitation (R90D) and extremely wet day precipitation (R95D) are used to describe the frequency of precipitation extremes. Consecutive dry days (CDD) and consecutive wet days (CWD) are indices used to quantify the duration of the wettest and driest parts of the year. Here, a rainy day was defined as the day with precipitation P 0.1 mm/day. We note that there are certain differences in the specific definition of extreme precipitation indices adopted in this study and those recommended by the World Meteorological Organization (WMO). For example, the WMO suggests to analyze the changing patterns of the maximum 5-day precipitation amount (Zhang et al., 2011; Limsakul and Singhruck, 2016), while the maximum 3-day precipitation amount is used in this study. Because precipitation with a duration of 1–3 days is the predominant precipitation event in the basin (Huang et al., 2014c). In addition, the WMO defines heavy precipitation and very heavy precipitation as those with rainfall larger than 10 mm and 25 mm, respectively, which are not applicable (Liu et al., 2013) in this study due to the arid and semi-arid climate in the WRB. Therefore, we acknowledge that the results obtained in this study might depend on the specific definition of the extreme precipitation indices. 3.2. Modified Mann-Kendall trend test method The modified Mann-Kendall trend (MMK) test (Mann, 1945; Kendall, 1955; Hamed and Rao, 1998) was adopted to assess the trends of precipitation extremes in the WRB. Although the original
Mann-Kendall (MK) test has been widely used and recommended by the WMO, it fails to deal with the issue of persistence in the hydro-meteorological time series. Hamed and Rao (1998) improved the MK test by taking the lag-i autocorrelation into consideration; and the MMK has been shown to be robust in capturing the trends of the hydro-meteorological time series. Details about MMK can be found in Huang et al. (2014b). The trend obtained by the MMK test was assessed at the 95% confidence level. 3.3. The heuristic segmentation method To examine the stationarity of extreme precipitation indices, the heuristic segmentation method proposed by Bernaola-Galván and Ivannov (2001) was introduced to detect the abrupt change points of the precipitation extreme time series. Compared with traditional methods such as the sliding T/F test, MK test, and rank sum test, this method performs better in dealing with highly nonlinear time series (Bernaola-Galván and Ivannov, 2001). Based on the sliding T test, this method was modified to distinguish the change point of nonlinear and non-stationary time series. For a time series with n observations, X ¼ x1 ; x2 ; . . . ; xn , the mean values and standard deviations are calculated for the time sequences of each point i on the left x1 , s1 , and on the right x2 , s2 . To quantify the difference between the averages of these two series x1 and x2 , the statistical significance was computed by Student’s ttest statistic as follows:
x1 x2 TðiÞ ¼ sðiÞ
ð1Þ
where
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðn1 1Þ s1 þ ðn2 1Þ s2 1 1 sðiÞ ¼ þ n1 n2 n1 þ n2 2
ð2Þ
is the pooled variance, and n1 and n2 denote the length of the two parts. By repeating the above procedure, the largest T value is taken as a candidate for the change point. Subsequently, the statistical significance, P(tmax), corresponding to the largest T is computed as follows:
Pðtmax Þ f1 I½v =ðv þt2max Þ ðdv ; dÞgg
ð3Þ
where Ix (a, b) is the incomplete beta function. Based on the Monte Carlo simulations, g ¼ 4:19 ln N 11:54, d ¼ 0:40, and v = n 2. If the difference between the averages is statistically significant, namely P(tmax) is smaller than the threshold of P0(0.95), the time series will not be split. Otherwise, the time series will be split and the iteration of the above procedures continues until the acquired significant value is smaller than the threshold or the length of the acquired segments is shorter than the minimum segment length l0 (l0 P 25).
Table 2 Definition of precipitation indices in this study.
a b
NO.
Index
Descriptive name
Definition
Units
1 2 3 4 5 6 7 8
RX1day RX3days R90 R95 R90D R95D CDD CWD
Maximum 1-day precipitation Maximum 3-day precipitation Very wet day precipitation amount Extremely wet day precipitation amount frequency of very wet day precipitation frequency of extremely wet day precipitation Consecutive drya days Consecutive wetb days
Annual maximum precipitation in one day Annual maximum precipitation of 3 consecutive days Annual precipitation total of precipitation exceeding 90th percentile Annual precipitation total of precipitation exceeding 95th percentile Annual number of rainy days with precipitation exceeding 90th percentile Annual number of rainy days with precipitation exceeding 95th percentile Maximum number of consecutive days with precipitation ＜0.1 mm Maximum number of consecutive days with precipitation 0.1 mm
mm mm mm mm day day day day
Dry day is the day with precipitation P ＜ 0.1 mm/day. Wet day is the day with precipitation P 0.1 mm/day.
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3.4. Cross wavelet analysis The cross wavelet analysis, introduced by Torrence and Compo (2010), is a new technique that integrates the wavelet transform and cross spectrum analysis. Compared with traditional methods (e.g., Fourier transform), it is more appropriate to capture the change characteristics and coupled oscillations of two time series both in the time and frequency domains (Hudgins et al., 1993; Torrence and Compo, 2010; Huang et al., 2015). However, one shortcoming of the cross wavelet analysis is the existence of edge artifacts because the wavelet might not be completely localized in time (Grinsted et al., 2004). Here, the cone of influence (COI) was introduced and taken as an area, where the wavelet power caused by a discontinuity at the edge dropped to e2 of the value at the edge (Grinsted et al., 2004). The calculation procedures of the cross wavelet analysis can be found in Torrence and Compo (2010), and the relevant codes can be downloaded from http://noc.ac.uk/usingscience/crosswavelet-wavelet-coherence. 4. Results and discussion 4.1. Changes in precipitation extremes 4.1.1. Changes in the magnitude and frequency of extreme precipitation events Fig. 2 shows the spatial distribution of the long-term mean of the indices across the WRB. Fig. 2 demonstrates that the precipitation extremes have a strikingly irregular spatial distribution in terms of the magnitude and frequency across the WRB. The areal averages of RX1day, RX3days, R90, R95, R90D, and R95D in the WRB are 52.67 mm, 73.98 mm, 291.61 mm, 197.17 mm, 10.86 days, and 5.68 days, respectively. The highest mean values of these indices are found in the middle and lower reaches of the WRB. Since the capital city Xi’an and other cities of Shaanxi province are situated in the mainstream, extreme precipitations are
likely attributed to the urban heat island effects (Kishtawal et al., 2010; Limsakul and Singhruck, 2016). In general, no significant trend was found for the historical changes in RX1day, RX3days, R90, R95, and R95D for the whole WRB. However, for the upstream of the WRB, significant trends have been detected; MMK statistics for RX1day, RX3days, R90, R95 and R95D are 2.79, 2.55, 3.18, 3.32, 2.27, and 3.14, respectively. In addition, a significant decrease trend of R95D was detected at 2 stations located upstream and downstream (Fig. 2E). 4.1.2. Changes in the extreme precipitation events durations The areal mean of CDD and CWD in the WRB is approximately 22.08 days and 13.41 days, respectively. Fig. 3 illustrates the spatial distribution of the MMK statistics of CDD and CWD. The MMK statistics indicate that there is no significant trend of CDD; they are all smaller than 1.96 at the 95% confidence level (Fig. 3A). The average CDD of all stations together for the 51-year period (1960–2010) shows that the CDD in the WRB ranges from approximately 23 days to 40 days, with a longer CDD mainly in the middle of the upstream and outlet of the downstream (Fig. 3A). With respect to the changes in CWD, statistically significant decreasing trends were observed at nine stations (Fig. 3B), which are mainly concentrated in the north of the upstream, midstream, and downstream of the basin. The long-term mean CWD ranges from seven days to ten days, with a shorter CWD dominating the northern WRB and the densely populated Xi’an city. The results suggest that extreme precipitation events in the WRB are characterized by a significant decrease of consecutive wet days, which contributed to the overall reduction of the annual precipitation in the WRB. 4.2. Diagnosis of non-stationarity of extreme precipitation events in the WRB Within the past several decades, intensification of human activities (e.g., urbanization, land use change, and construction of water
Fig. 2. The MMK trend tests and long-term mean values of RX1day, RX3days, R90, R95, R90D and R95D across the WRB. The blue upward triangles denote positive MMK statistics, while the blue downward triangles indicate the negative MMK statistics. The blue triangles with black dots indicate the trends are significant at the 95% confidence level.
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Fig. 3. Same as Fig. 2. But for CDD and CWD.
conservancy projects) and climate change (global warming) were observed in the WRB (Chang et al., 2014; Huang et al., 2014b,c), which might have led to the non-stationarity of the extreme precipitation events. Here, the heuristic segmentation method, introduced in Section 3.3, was applied to detect the change points of the extreme precipitation indices in the WRB and five subregions. The threshold P0 was set to 0.95 and ‘0 was set to 25. For brevity, Fig. 4 only shows the segmentations and change points of the CDD in the upstream. The blue line in Fig. 4 indicates the first iteration and segmentation process. A change point (1969) was identified, with P(tmax) = 1 > P0. The dotted purple line in Fig. 4 denotes the second iteration and segmentation process. Notably, the second change point was detected in 2002, with P(tmax) = 0.9504 > P0. The solid green line with yellow dots refers to the third iteration and segmentation process, and the third change point was determined in 1994, with P(tmax) = 0.9589 > P0. Subsequently, the segmentation process stopped because the length of the segments became smaller than ‘0 . Based on the same procedures mentioned above, the change points for all extreme precipitation indices in the WRB and five sub-regions were calculated; they are summarized in Table 3. Most of the breakpoints occurred in the late 1960s (1965, 1967, 1968 and 1969). Spatially, the upstream experienced more viable climate conditions because change points were detected for all indices: RX1day in 1967, RX3days, R90, R95, R90D, and R95D in 1969; CDD in 1969, 1994, and 2002, and CWD in 1965 and 2003. In addition, CWD has change points in all regions except for the
Jinghe Zone (Table 4), which implies that the CWD has considerably changed during the past decades. The results indicate that the stationarity assumption of the precipitation extremes is invalid for the study region. Notably, the change point of CWD in the upstream in 2003 corresponds to the devastating flood year with total economic losses of up to 0.5 billion dollars (Liang, 2008). This means that the flood disaster could be a response to the non-stationarity of CWD characterized by significant decreasing trends. With respect to other extreme precipitation indices, such as RX1day, RX3days, R90, R95, R95D, and CDD, their non-stationarity in the upstream is consistent with their significant trends. Hence, the stationarity of extreme precipitation indices has been dampened by their significant trends. 4.3. Correlations between the large-scale ocean -atmospheric circulation and extreme precipitation events in the WRB Both PDO and ENSO events show strong relations with extreme precipitation events, as observed worldwide. Investigating the detailed linkages, especially the evolution of the relations, helps us to better understand extreme precipitation variations, which has great implications for precipitation extreme predictions and water resources management. Here, the cross wavelet analysis was applied to study the connections between PDO/ENSO events and RX1day, R90D, and CWD (Figs. 5and 6, respectively), which represent the magnitude, frequency, and duration of precipitation extremes, respectively.
Fig. 4. Segmentations and change points of CDD in the upstream of the WRB. The blue line, the dotted purple line and the solid green line with yellow dots refer to the first, second and third iteration and segmentation process, respectively.
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Table 3 Results of change points of the extreme precipitation indices in the WRB and five sub-regions. Index
RX1day RX3days R90 R95 R90D R95D CDD CWD
Region The WRB
Upstream
Midstream
Downstream
Jinghe
Beiluohe
– – – – – – 1968 1969
1967 1969 1969 1969 1969 1969 1969; 1994; 2002 1965;2003
– – – – – – – 1965;2004
– – – – – – – 1965
2009 – – – – – – –
– – – – – – – 1982
Table 4 Results of the design rainstorm of different return period using the non-stationary RX1day time series and series after change point. Time series type
Non-stationary RX1day Series before the mutation Series after the mutation
Design value Return period (Year) 5
10
50
32.51 39.36 30.21
37.24 47.34 34.19
47.12 67.48 42.42
4.3.1. Teleconnection between PDO and extreme precipitation events Fig. 5 exhibits the cross wavelet transforms between PDO and precipitation extremes in the WRB. Fig. 5A shows that the PDO has a statistically significant positive correlation with the RX1day variations at the 95% confidence level, with a 1–3 year signal from 1981 to 1989 and a 3–5 year signal from 1990 to 2005, which means that PDO has a strong association with changes in extreme precipitation in the WRB. Fig. 5B indicates that the PDO has a statistically significant positive correlation with R90D variations in the WRB at the 95% confidence level, with a 3–5 year signal from 1985 to 1987 and a 2 year signal from 1999 to 2000. Fig. 5C demonstrates that the PDO has a statistically significant positive correlation with CWD variations at the 95% confidence level, with a 3 year signal from 1993 to 1995. These findings suggest that the changes in the frequency and duration of extreme precipitation events in the WRB are significantly linked to the PDO. However, the correlations between PDO and CWD/R90D are relatively weak when compared with those of RX1day for the whole basin and the five sub-regions. Note that the PDO is a long-lived El Niño-like pattern of the Pacific climate variability and the linkages between PDO and precipitation are not consistent with the duration of the PDO. However, a long-periodic series does not mean that its influences on other variables are also long-periodic. As a matter of fact, the time series is composed of many signals with different frequencies (Partal, 2010; Nalley et al., 2012). The advantage of the cross wavelet analysis is to reveal the relationships between two time variables in different time and frequency domains. In addition, some relevant papers (Brownf and Comrie, 2002; Huang et al., 2015, 2016) reported the linkage between PDO and precipitation at 1–3 and 3–5 year intervals, which further verifies the reliability of our findings in this study. 4.3.2. Teleconnection between ENSO and extreme precipitation events The cross wavelet transforms between ENSO events and precipitation extremes in the WRB are displayed in Fig. 6. Obviously, the ENSO events exhibit a statistically negative correlation with RX1day in the WRB with a 3–4 year signal from 1969 to 1971, and positive correlation with 2–5 year signal from 1981 to 1991 and a 3– 6 year signal in 1995–2005 at the 95% confidence level (Fig. 6A). The results indicate that ENSO events played an important role
in triggering intensive precipitation in the WRB. To gain additional insights, we repeated the analysis based on the Pearson and Spearman correlation coefficients. The Pearson and Spearman correlation coefficients between RX1day and MEI from 1960 to 2010 are 0.10 and 0.08, respectively, which are low and insignificant. However, the Pearson and Spearman correlation coefficients of RX1day and MEI from 1969 to 1971 are -0.63 and -0.50, respectively, and those from 1995 to 2005 are 0.34 and 0.55, respectively, which are significant at the 95% confidence level. These significant correlations between the RX1day and MEI series from 1969 to 1971 and 1995 to 2005 were captured by the cross wavelet analysis, demonstrating the importance of considering various time scales when investigating the PDO/ENSO impact on regional precipitation extremes. Similarly, Fig. 6B shows that the ENSO events exhibit a statistically significant positive correlation with R90D variations in the WRB with a 3 year signal at the 95% confidence level from 1966 to 1969 and a 3–5 year signal from 1983 to 1990. This suggests that the ENSO events are closely related to the frequency of extreme precipitation events in this area. Fig. 6C illustrates that the ENSO events show a statistically significant negative correlation with the CWD variations in the WRB with a 1–4 year signal at the 95% confidence level from 1967 to 1971. In addition, ENSO events have a statistically significant positive correlation with the CWD with a 13–14 year signal at the 95% confidence level from 1970 to 1980, implying that the ENSO events are closely associated with longlasting rainy days in the WRB. In addition, the change point of the CWD (Section 4.2) in 1969 in the WRB corresponds to the period when ENSO events are statistically negative correlated with the CWD index from 1967 to 1971. This phenomenon was also observed for the five sub-regions (for brevity, relevant charts are not shown). In general, ENSO events were found to have strong impacts on the nonstationary extreme precipitation series. The comparison of Figs. 5 and 6 shows that the relation between the PDOI and R90D/CWD is not as strong as that between MEI and R90D/CWD. This indicates that the frequency and duration of precipitation extremes in the WRB is influenced more by ENSO events than PDO. This might be attributed to the limitations of the relatively short record length and uncertainty from the potential interaction between PDO and ENSO (Kiem and Verdon-Kidd, 2009). Besides, both PDO and ENSO events have a statistically positive correlation with RX1day from 1981 to 1989 and 1995 to 2005 and R90D from 1985 to 1987. These findings demonstrate that the intensity and frequency of precipitation extremes in the WRB is closely linked to both PDO and ENSO events. We acknowledge that PDO can influence precipitation variability indirectly via modulation of ENSO impacts (Verdon et al., 2004; Kiem and VerdonKidd, 2009). However, the interaction between the PDO and ENSO events cannot be well quantified by the Wavelet analysis method as used in this study. Hence, to what extent ENSO’s impacts on precipitation is modulated by PDO requires further investigations in
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Fig. 5. The cross wavelet transforms between the PDO and precipitation extremes in the WRB: (A) RX1day, (B) R90D and (C) CWD. The thick black contour denotes the relations which are significant at the 95% confidence level against the red noise. The cone of influence (COI) where edge effects might distort the picture is shown as lighter shades. The relative phase relationship is indicated by the arrow direction (with anti-phase pointing left, in-phase pointing right). The color bar on the right denotes the wavelet energy.
the future. We also explored the relationship between the precipitation extremes with other ocean-atmospheric circulation patterns, such as the Atlantic Multi-decadal Oscillation (AMO), but found no evident signal (results not shown).
4.4. Discussion Changes in extreme precipitation events have attracted increasing attention, especially with respect to changing environments.
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Fig. 6. Same as Fig. 5, but for ENSO events: (A) RX1day, (B) R90D and (C) CWD.
However, very few studies investigated the validity of the nonstationarity assumption of precipitation extremes. This paper complements previous studies by focusing on the non-stationarity of extreme precipitation events in the WRB, a typical arid and semiarid climate zone in China. Several change points of the precipitation extremes indices were revealed, implying that the stationarity
assumption of precipitation extremes in the WRB is invalid, especially in the upstream, where change points were detected for all indices. These results are broadly consistent with the observed abrupt changes in precipitation extremes during the 1960s, at 1980, and the 1990s across China (Tu et al., 2010; Chen et al., 2014).
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Fig. 7. Segmentations and change points of annual precipitation in the WRB (A) and in the upstream (B). The blue line refers to the iteration and segmentation process. Since P (tmax) is smaller than the threshold of P0 = 0.95, the segmentation process stops for the first iteration.
Fig. 8. Comparison of the time series before (solid blue line) and after (solid red line) mutation point of RX1day in the upstream. The dashed lines are for the averages of the two time series.
Using the heuristic segmentation method, the stationarity of the annual precipitation in the WRB and upstream were diagnosed and shown in Fig. 7. The blue line in Fig. 7 indicates the iteration and segmentation process; P(tmax) is smaller than the threshold of P0, with 0.9013 in the WRB and 0.9401 in the upstream. The segmentation process stopped, indicating that there was no change point of annual precipitation. This suggests that, compared with the annual precipitation, precipitation extremes were much more sensitive to the changing environments. Therefore, more attention should be paid to extreme precipitation events in the basin, not only because of the tremendous damage by extreme events, but also because of the non-stationarity of the changing environment. To further explore the impact of the non-stationary assumption of the extreme precipitation series, changes in the RX1day series in the upstream are taken as an example, because a change point was detected. Fig. 8 shows the averages of the time series before and after the change point of RX1day in the upstream. The difference
between the two series is notable, with a much smaller mean value after the change point than before. Assuming that the RX1day series follows the Pearson III type curve distribution, we estimated the design precipitation at different return periods using the non-stationary RX1day time series and the series before and after the change point. The results are summarized in Table 4. Table 4 shows that the design value becomes much higher based on the series before the change point, and vice versa based on the series after the change point. The first case might lead to the overinvestment of water projects, whilst the second case might increase the risk of hydraulic work. This means that the assumption of non-stationarity of extreme events could lead to general uncertainties in hydraulic engineering designs and operations. We note that the precipitation intensityfrequency-duration (IFD) relationship, a commonly used method for the design and planning of water supply and management systems worldwide (Wang et al., 2013; Verdon-Kidd and Kiem, 2015),
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is derived based on the assumption of stationarity. With a nonstationary precipitation series, the IFD might result in the failure of design criteria and water management (Verdon-Kidd and Kiem, 2015). Thus, the non-stationarity of extreme precipitation should be explicitly considered and appropriately addressed (VerdonKidd and Kiem, 2015). We emphasize the need to develop a more general non-stationary analysis framework and the importance of modifying relevant guidelines for flood protection design, given the potential of non-stationarity in changing environments (Kiem and Verdon-Kidd, 2013; Madsen et al., 2014).
5. Conclusions The investigation of extreme precipitation events and their changing patterns and causes is of great significance to better understand the regional response of the precipitation to changing environments. In this study, several indices were developed to describe extreme precipitation in the WRB based on which the trends were examined. The non-stationarity of extreme precipitation events was then investigated by examining the changing points of the time series. Subsequently, we studied the linkages between extreme precipitation events and large-scale oceanatmospheric circulation patterns to explore the potential causes behind the variation of precipitation extremes. Generally, no change point was detected in the annual precipitation, while several change points were found in the precipitation extremes. This indicates that extreme precipitation events are much more sensitive to changing environments compared with the annual precipitation. The results imply that assumption of stationary series could lead to severe biases in extreme hydrological frequency analysis, which further affects local/regional hydraulic engineering designs and water resources management. Extreme precipitation events have significant relations with both PDO and ENSO events. This suggests that the large-scale ocean-atmospheric circulation index has the potential to improve the prediction of extreme precipitation in the study region. Notably, ENSO events showed stronger relations with extreme precipitation than PDO. However, caution should be exercised in interpreting the impact of PDO or ENSO on extreme precipitation, given the potential interactions between PDO and ENSO events and other non-climatic factors (e.g., urbanization and topography), which were ignored in this study. This study emphasizes the importance of considering the non-stationarity of extreme series in hydrological frequency analysis. We highlight the asymmetric impact of ENSO/PDO on the frequency, duration, and intensity of precipitation extremes, which has great implications for the prediction and management of extreme hydrological events.
Acknowledgement This research was supported by the National Natural Fund Major Research Plan (91325201), the National Department Public Benefit Research Foundation of Ministry of Water Resources (201501058), the National Major Fundamental Research Program (2011CB403306-2), the National Natural Fund Major Research Plan (51190093), and Project of School of water resources and hydropower of Xi’an University of Technology (2016ZZKT-15).
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Research papers
Identification of the non-stationarity of extreme precipitation events and correlations with large-scale ocean-atmospheric circulation patterns: A case study in the Wei River Basin, China Saiyan Liu a, Shengzhi Huang a,⇑, Qiang Huang a, Yangyang Xie a, Guoyong Leng b, Jinkai Luan a, Xiaoyu Song a, Xiu Wei a, Xiangyang Li c a
State Key Laboratory Base of Eco-Hydraulic Engineering in Arid Area, Xi’an University of Technology, Xi’an 710048, China Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China c Northwest Engineering Corporation Limited, Xi’an 710065, China b
a r t i c l e
i n f o
Article history: Received 30 October 2016 Received in revised form 3 March 2017 Accepted 6 March 2017 Available online 8 March 2017 This manuscript was handled by Corrado Corradini, Editor-in-Chief, with the assistance of Philip Brunner, Associate Editor Keywords: Extreme precipitation Non-stationarity Heuristic segmentation method Change point Wei River Basin
a b s t r a c t The investigation of extreme precipitation events in terms of variation characteristics, stationarity, and their underlying causes is of great significance to better understand the regional response of the precipitation variability to global climate change. In this study, the Wei River Basin (WRB), a typical ecoenvironmentally vulnerable region of the Loess Plateau in China was selected as the study region. A set of precipitation indices was adopted to study the changing patterns of precipitation extremes and the stationarity of extreme precipitation events. Furthermore, the correlations between the Pacific Decadal Oscillation (PDO)/El Niño-Southern Oscillation (ENSO) events and precipitation extremes were explored using the cross wavelet technique. The results indicate that: (1) extreme precipitation events in the WRB are characterized by a significant decrease of consecutive wet days (CWD) at the 95% confidence level; (2) compared with annual precipitation, daily precipitation extremes are much more sensitive to changing environments, and the assumption of stationarity of extreme precipitation in the WRB is invalid, especially in the upstream, thereby introducing large uncertainty to the design and management of water conservancy engineering; (3) both PDO and ENSO events have a strong influence on precipitation extremes in the WRB. These findings highlight the importance of examining the validity of the stationarity assumption in extreme hydrological frequency analysis, which has great implications for the prediction of extreme hydrological events. Ó 2017 Elsevier B.V. All rights reserved.
1. Introduction There is consensus that global warming and the intensification of human activities (e.g., urbanization, irrigation, afforestation) tend to accelerate the global hydrological cycle, resulting in more frequent and severe hydroclimate extremes, such as floods, flashfloods, or droughts with devastating impacts on local settlements, agriculture, ecosystems, industry, and socio-economic development (Trenberth, 2011; Toreti and Desiato, 2008; Choi et al., 2009; Santos et al., 2011; Wang et al., 2012; Huang et al., 2014a; Leng et al. 2015a,b). The situation is even worse in developing countries because of the higher vulnerability to disasters due to the highly-dense population and imperfect drainage infrastructure
⇑ Corresponding author. E-mail address: [email protected] (S. Huang). http://dx.doi.org/10.1016/j.jhydrol.2017.03.012 0022-1694/Ó 2017 Elsevier B.V. All rights reserved.
(Croitoru et al., 2015). According to the fifth Intergovernmental Panel on Climate Change (IPCC) report, the intensity of extreme precipitation events will increase with global warming at a rate of approximately 7%°C1, much faster than the response of the mean precipitation (1–3%°C1; IPCC, 2013; Stocker et al., 2013). Therefore, an increasing number of studies emphasized the importance of investigating extreme precipitation events (Trenberth, 2011; Tabari et al., 2014; Madsen et al., 2014). On the global scale, Westra et al. (2014) reported an increase in the extreme rainfall intensity, with the magnitude of changes depending on the geographic location and duration of the events. Similarly, changing precipitation properties have also been studied in various regions of the world. Based on daily precipitation datasets and the Mann-Whitney U test, Verdon-Kidd and Kiem (2015) showed the statistically significant shift of the annual maximum daily rainfall at most stations across Australia. Besselaar et al.
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(2012) observed a general increase in extreme precipitation in Northern Europe in autumn, winter, and spring. A significant decrease of precipitation days, and increase of heavy precipitation events have also been confirmed in Romania (Croitoru et al., 2015). Limsakul and Singhruck (2016) claimed that the precipitation events became less frequent but more intense in Thailand. Based on various precipitation indices, Goswami et al. (2006) discovered a significant increase of extreme precipitation events both in frequency and magnitude and a significant decrease of the frequency of moderate precipitation events over central India during the monsoon seasons from 1951 to 2000. In China, both annual maximum daily precipitation and heavy precipitation days exhibit an increasing trend in the southern river basins, but a decrease in the northern river basins (Chen et al., 2011). In Canada, no evident trends in extreme precipitation, neither in frequency or intensity, have been found in the last century (Zhang et al., 2001). This means that the response of hydrological components to global climate change varies greatly on the regional scale. Hence, it is necessary to investigate precipitation variations from a regional standpoint. It was suggested that the design criteria of urban drainage infrastructures, which are based on the frequency-durationintensity curve in a certain period, should be revised because of the increasing intensity and frequency of precipitation extremes (Kiem and Verdon-Kidd, 2013; Wang et al., 2013). The traditional hydrological frequency analysis of extreme precipitation and floods is based on the assumption of stationarity (Kiem and Verdon-Kidd, 2013; Verdon-Kidd and Kiem, 2015). This means that historical features of extreme events can be used to derive extreme value statistics in the future (Madsen et al., 2014). However, changing environments (e.g., global warming and/or anthropogenic activities) might alter the statistical characteristics of hydroclimate time series (Verdon et al., 2004; Kiem and VerdonKidd, 2009), resulting in so-called non-stationarity (Jiang et al., 2015). Ignoring the stationarity would therefore lead to severe biases in the extreme event assessment (Wigley, 1985; Kiem and Verdon-Kidd, 2013; Jiang et al., 2015). Furthermore, hydrological modelling studies would suffer inevitable bias without accounting for the non-stationarity of hydroclimate data based on which hydrological model parameters are tuned. However, to date, relatively a few studies have addressed the stationarity of extreme precipitation events. Extreme precipitation is induced by the combined influences of climatic and non-climatic factors (e.g., global warming, land reclamation, land cover, ocean-atmospheric circulation patterns, and urbanization; Croitoru et al., 2015; Sun et al., 2015). Among these factors, the variation of large-scale ocean-atmospheric circulation patterns is recognized as one of the most important factors affecting the generation of precipitation (Verdon et al., 2004; Kiem and Verdon-Kidd, 2009; Kenyon and Hegerl, 2010; Ward et al., 2014; Croitoru et al., 2015; Verdon-Kidd and Kiem, 2015; Limsakul and Singhruck, 2016). For instance, Limsakul and Singhruck (2016) showed that El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) are the remote drivers of the extreme precipitation variability in Thailand. However, the methods adopted in previous studies to investigate the teleconnections between precipitation extremes and ocean-atmospheric circulation are commonly based on the correlation coefficient (e.g., Spearman’s rank order correlation), which cannot comprehensively reflect the changing relations (Huang et al., 2015). The Wei River Basin (WRB), the largest tributary of the Yellow River Basin (YRB), was selected as the study region. It is located in the middle reaches of the YRB and comprises a total drainage area of 135,000 km2. As the main grain-yielding and important industrial and commercial area in Northwest China, the basin is a major water supply source, which directly supports a popula-
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tion of 22 million people. With the establishment of the Guangzhong-Tianshui Economic Zone, it acts as a strong stimulus for the economic development of West China. The intensification of the hydrological cycle would further alter the spatial and temporal distribution of precipitation, thereby increasing the uncertainty of water supply in the basin (Huang et al., 2014b,c). Hence, an improved knowledge of extreme precipitation changes is required for effective water resources management. Moreover, the Loess Plateau in the northern WRB contains the most highly erosion-prone soil with fragile ecological conditions in the world. Extreme precipitation could give rise to the acceleration of soil erosion. Therefore, a comprehensive study of the precipitation extreme changes is of great significance for ecological restoration. Previous studies have investigated the precipitation trend and pattern in the WRB (Chang et al., 2014; Huang et al., 2014c). However, few studies have been conducted to identify the nonstationarity of extreme precipitation events and explore the associations with large-scale ocean-atmospheric circulation patterns in this basin. The main objectives of this study are: (1) to test if extreme precipitation events are still stationary under changing environments and (2) to explore if there are significant relationships between precipitation extremes and large-scale ocean-atmospheric circulation patterns. The primary novelty of this study is as follows: (1) Previous studies investigating the relations between the precipitation extremes and ocean-atmospheric circulation were commonly based on the correlation coefficient (e.g., Spearman’s rank order correlation), largely ignoring the changes or evolution of the relations. In this study, we adopt cross wavelet analysis, which can help us to comprehensively reveal the relationships in various time and frequency domains; (2) Furthermore, we choose several extreme indices covering the frequency, duration and intensity of extremes to investigate which specific aspects of extremes are more affected by ENSO or PDO. This has broad implications to better understand the relations between the precipitation extremes and ocean-atmospheric circulation. Although the WRB was selected as the study region, the methods adopted in this study can be applied to other regions. The remainder of the paper is organized as follows: Section 2 describes the study area and data collection. Section 3 focuses on the methods used in this study. The results and discussion are provided in Section 4, and the conclusions are presented in Section 5.
2. Study area and data 2.1. Study area The WRB (Fig.1) originates in the Gansu Province and cuts through the Shaanxi Province, extending over a latitude of 4° (between 33.5°N and 37.5°N) and longitude of 8° (between 103.5°E and 110.5°E). The Jinghe and Beiluohe rivers are the two largest tributaries of the basin, with drainage areas of 4.5 104 km2 and 2.69 104 km2, respectively. Located in a semi-dry and semi-humid monsoon climate zone, the basin is characterized by distinctive seasonality with abundant precipitation and high temperatures in summer, and sparse precipitation and low temperatures in winter. The annual precipitation over the basin is approximately 600 mm; 60% of precipitation falls in the flood season (from June to September). The mean annual temperature ranges from 7.8 °C to 13.5 °C (Chang et al., 2014). To investigate the long-term variation of precipitation extremes, the whole basin was divided into five sub-regions: upstream, midstream, downstream, Jinghe Region, and Beiluohe Region (Fig. 1).
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Fig. 1. The location of the meteorological and hydrological stations in the WRB and its five sub-basins.
2.2. Data The daily precipitation for 1960–2010 was obtained from the National Climate Center (NCC) of the China Meteorological Administration (CMA). A total of 21 meteorological stations were used, 15 of which are within the WRB and six (i.e., Lintao, Guyuan, Foping, Zhenan, Shangzhou and Yanan) are outside but close to the edges of the basin. The detailed information is listed in Table 1. The weather stations used in the study have a reasonable spatial coverage and include all types of topography and climatic regions in the WRB (Fig. 1 and Table 1). Therefore, regional features of the precipitation extreme variability in the WRB could be detected. Precipitation data were missing at six stations. However, the total missing data, which were replaced by the average values of the same day from neighboring stations, are less than 0.01%. Note that the length
of observed records might not be long enough to identify the change characteristics of extreme precipitation events of longer return periods (e.g., 1 in 100 years of events), which is out of the scope of this study. Nevertheless, these data are sufficient for the purpose of change detection (Partal, 2010; Nalley et al., 2012), which is the main aim of this study. To examine the relationships between the precipitation extremes and the large-scale ocean-atmospheric circulation patterns, the correlations between the extreme precipitation indices and ENSO/PDO indices were investigated. The monthly PDO index (PDOI) is defined as the leading principal component of the North Pacific monthly sea surface temperature variability, which is closely related to the variability of extreme precipitation (Limsakul and Singhruck, 2016; Voskresenskaya and Vyshkvarkova, 2016). Several indices are available for the representation of ENSO phe-
Table 1 The information on weather stations in the WRB.
a
NO.
Weather stationa
Time of establishment
Latitude (N)
Longitude (E)
Altitude (m)
Period
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Baoji Changwu Foping Guyuan Huajialing Huanxian Huashan Lintao Luochuan Minxian Pingliang Shangzhou Tianshui Tongchuan Wugong Wuqi Xi’an Xifengzhen Xiji Yanan Zhenan
1951.9 1956.9 1957.1 1956.1 1951.1 1957.1 1953.1 1951.1 1954.11 1951.1 1951.1 1953.3 1951.1 1955.1 1954.4 1956.1 1951.1 1951.1 1957.2 1951.1 1957.1
34.35 35.20 33.52 36.00 35.38 36.58 34.48 35.35 35.82 34.43 35.55 33.87 34.58 35.08 34.25 36.92 34.30 35.73 35.97 36.60 33.43
107.13 107.80 107.98 106.27 105.00 107.30 110.08 103.85 109.50 104.02 106.67 109.97 105.75 109.07 108.22 108.17 108.93 107.63 105.72 109.50 109.15
612 1206 827 1753 2450 1255 2064 1893 1159 2315 1346 742 1141 978 447 1331 397 1421 1916 958 693
1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010 1960–2010
Stations are ranged in alphabetical order.
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nomena (Kiem and Franks, 2001), such as the NINO l, NINO 3, NINO 3.4, Southern Oscillation Index (SOI; Ward et al., 2016), and Multivariate ENSO Index (MEI). In this paper, the monthly MEI time series was used for the investigation of the teleconnection between the extreme precipitation events and ENSO events. The MEI index was selected because it outperforms other indices by integrating more information than others (Kiem and Franks, 2001; Limsakul and Singhruck, 2016). Here, the monthly PDO data were obtained from http://research.jisao.washington.edu/ pdo/, while the monthly MEI were downloaded from http:// www.esrl.noaa.gov/psd/enso/mei/table.html. 3. Methodology 3.1. Precipitation indices In this study, eight extreme precipitation indices were defined, which can be classified into intensity, frequency, and duration indices. Detailed descriptions about the extreme precipitation indices can be found in Table 2. Specifically, the maximum 1-day and 3day precipitation amount (RX1day and RX3days), very wet day precipitation amount (R90), and extremely wet day precipitation amount (R95) are the indices to characterize the intensity of extreme precipitation events, whereas the frequency of very wet day precipitation (R90D) and extremely wet day precipitation (R95D) are used to describe the frequency of precipitation extremes. Consecutive dry days (CDD) and consecutive wet days (CWD) are indices used to quantify the duration of the wettest and driest parts of the year. Here, a rainy day was defined as the day with precipitation P 0.1 mm/day. We note that there are certain differences in the specific definition of extreme precipitation indices adopted in this study and those recommended by the World Meteorological Organization (WMO). For example, the WMO suggests to analyze the changing patterns of the maximum 5-day precipitation amount (Zhang et al., 2011; Limsakul and Singhruck, 2016), while the maximum 3-day precipitation amount is used in this study. Because precipitation with a duration of 1–3 days is the predominant precipitation event in the basin (Huang et al., 2014c). In addition, the WMO defines heavy precipitation and very heavy precipitation as those with rainfall larger than 10 mm and 25 mm, respectively, which are not applicable (Liu et al., 2013) in this study due to the arid and semi-arid climate in the WRB. Therefore, we acknowledge that the results obtained in this study might depend on the specific definition of the extreme precipitation indices. 3.2. Modified Mann-Kendall trend test method The modified Mann-Kendall trend (MMK) test (Mann, 1945; Kendall, 1955; Hamed and Rao, 1998) was adopted to assess the trends of precipitation extremes in the WRB. Although the original
Mann-Kendall (MK) test has been widely used and recommended by the WMO, it fails to deal with the issue of persistence in the hydro-meteorological time series. Hamed and Rao (1998) improved the MK test by taking the lag-i autocorrelation into consideration; and the MMK has been shown to be robust in capturing the trends of the hydro-meteorological time series. Details about MMK can be found in Huang et al. (2014b). The trend obtained by the MMK test was assessed at the 95% confidence level. 3.3. The heuristic segmentation method To examine the stationarity of extreme precipitation indices, the heuristic segmentation method proposed by Bernaola-Galván and Ivannov (2001) was introduced to detect the abrupt change points of the precipitation extreme time series. Compared with traditional methods such as the sliding T/F test, MK test, and rank sum test, this method performs better in dealing with highly nonlinear time series (Bernaola-Galván and Ivannov, 2001). Based on the sliding T test, this method was modified to distinguish the change point of nonlinear and non-stationary time series. For a time series with n observations, X ¼ x1 ; x2 ; . . . ; xn , the mean values and standard deviations are calculated for the time sequences of each point i on the left x1 , s1 , and on the right x2 , s2 . To quantify the difference between the averages of these two series x1 and x2 , the statistical significance was computed by Student’s ttest statistic as follows:
x1 x2 TðiÞ ¼ sðiÞ
ð1Þ
where
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðn1 1Þ s1 þ ðn2 1Þ s2 1 1 sðiÞ ¼ þ n1 n2 n1 þ n2 2
ð2Þ
is the pooled variance, and n1 and n2 denote the length of the two parts. By repeating the above procedure, the largest T value is taken as a candidate for the change point. Subsequently, the statistical significance, P(tmax), corresponding to the largest T is computed as follows:
Pðtmax Þ f1 I½v =ðv þt2max Þ ðdv ; dÞgg
ð3Þ
where Ix (a, b) is the incomplete beta function. Based on the Monte Carlo simulations, g ¼ 4:19 ln N 11:54, d ¼ 0:40, and v = n 2. If the difference between the averages is statistically significant, namely P(tmax) is smaller than the threshold of P0(0.95), the time series will not be split. Otherwise, the time series will be split and the iteration of the above procedures continues until the acquired significant value is smaller than the threshold or the length of the acquired segments is shorter than the minimum segment length l0 (l0 P 25).
Table 2 Definition of precipitation indices in this study.
a b
NO.
Index
Descriptive name
Definition
Units
1 2 3 4 5 6 7 8
RX1day RX3days R90 R95 R90D R95D CDD CWD
Maximum 1-day precipitation Maximum 3-day precipitation Very wet day precipitation amount Extremely wet day precipitation amount frequency of very wet day precipitation frequency of extremely wet day precipitation Consecutive drya days Consecutive wetb days
Annual maximum precipitation in one day Annual maximum precipitation of 3 consecutive days Annual precipitation total of precipitation exceeding 90th percentile Annual precipitation total of precipitation exceeding 95th percentile Annual number of rainy days with precipitation exceeding 90th percentile Annual number of rainy days with precipitation exceeding 95th percentile Maximum number of consecutive days with precipitation ＜0.1 mm Maximum number of consecutive days with precipitation 0.1 mm
mm mm mm mm day day day day
Dry day is the day with precipitation P ＜ 0.1 mm/day. Wet day is the day with precipitation P 0.1 mm/day.
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3.4. Cross wavelet analysis The cross wavelet analysis, introduced by Torrence and Compo (2010), is a new technique that integrates the wavelet transform and cross spectrum analysis. Compared with traditional methods (e.g., Fourier transform), it is more appropriate to capture the change characteristics and coupled oscillations of two time series both in the time and frequency domains (Hudgins et al., 1993; Torrence and Compo, 2010; Huang et al., 2015). However, one shortcoming of the cross wavelet analysis is the existence of edge artifacts because the wavelet might not be completely localized in time (Grinsted et al., 2004). Here, the cone of influence (COI) was introduced and taken as an area, where the wavelet power caused by a discontinuity at the edge dropped to e2 of the value at the edge (Grinsted et al., 2004). The calculation procedures of the cross wavelet analysis can be found in Torrence and Compo (2010), and the relevant codes can be downloaded from http://noc.ac.uk/usingscience/crosswavelet-wavelet-coherence. 4. Results and discussion 4.1. Changes in precipitation extremes 4.1.1. Changes in the magnitude and frequency of extreme precipitation events Fig. 2 shows the spatial distribution of the long-term mean of the indices across the WRB. Fig. 2 demonstrates that the precipitation extremes have a strikingly irregular spatial distribution in terms of the magnitude and frequency across the WRB. The areal averages of RX1day, RX3days, R90, R95, R90D, and R95D in the WRB are 52.67 mm, 73.98 mm, 291.61 mm, 197.17 mm, 10.86 days, and 5.68 days, respectively. The highest mean values of these indices are found in the middle and lower reaches of the WRB. Since the capital city Xi’an and other cities of Shaanxi province are situated in the mainstream, extreme precipitations are
likely attributed to the urban heat island effects (Kishtawal et al., 2010; Limsakul and Singhruck, 2016). In general, no significant trend was found for the historical changes in RX1day, RX3days, R90, R95, and R95D for the whole WRB. However, for the upstream of the WRB, significant trends have been detected; MMK statistics for RX1day, RX3days, R90, R95 and R95D are 2.79, 2.55, 3.18, 3.32, 2.27, and 3.14, respectively. In addition, a significant decrease trend of R95D was detected at 2 stations located upstream and downstream (Fig. 2E). 4.1.2. Changes in the extreme precipitation events durations The areal mean of CDD and CWD in the WRB is approximately 22.08 days and 13.41 days, respectively. Fig. 3 illustrates the spatial distribution of the MMK statistics of CDD and CWD. The MMK statistics indicate that there is no significant trend of CDD; they are all smaller than 1.96 at the 95% confidence level (Fig. 3A). The average CDD of all stations together for the 51-year period (1960–2010) shows that the CDD in the WRB ranges from approximately 23 days to 40 days, with a longer CDD mainly in the middle of the upstream and outlet of the downstream (Fig. 3A). With respect to the changes in CWD, statistically significant decreasing trends were observed at nine stations (Fig. 3B), which are mainly concentrated in the north of the upstream, midstream, and downstream of the basin. The long-term mean CWD ranges from seven days to ten days, with a shorter CWD dominating the northern WRB and the densely populated Xi’an city. The results suggest that extreme precipitation events in the WRB are characterized by a significant decrease of consecutive wet days, which contributed to the overall reduction of the annual precipitation in the WRB. 4.2. Diagnosis of non-stationarity of extreme precipitation events in the WRB Within the past several decades, intensification of human activities (e.g., urbanization, land use change, and construction of water
Fig. 2. The MMK trend tests and long-term mean values of RX1day, RX3days, R90, R95, R90D and R95D across the WRB. The blue upward triangles denote positive MMK statistics, while the blue downward triangles indicate the negative MMK statistics. The blue triangles with black dots indicate the trends are significant at the 95% confidence level.
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189
Fig. 3. Same as Fig. 2. But for CDD and CWD.
conservancy projects) and climate change (global warming) were observed in the WRB (Chang et al., 2014; Huang et al., 2014b,c), which might have led to the non-stationarity of the extreme precipitation events. Here, the heuristic segmentation method, introduced in Section 3.3, was applied to detect the change points of the extreme precipitation indices in the WRB and five subregions. The threshold P0 was set to 0.95 and ‘0 was set to 25. For brevity, Fig. 4 only shows the segmentations and change points of the CDD in the upstream. The blue line in Fig. 4 indicates the first iteration and segmentation process. A change point (1969) was identified, with P(tmax) = 1 > P0. The dotted purple line in Fig. 4 denotes the second iteration and segmentation process. Notably, the second change point was detected in 2002, with P(tmax) = 0.9504 > P0. The solid green line with yellow dots refers to the third iteration and segmentation process, and the third change point was determined in 1994, with P(tmax) = 0.9589 > P0. Subsequently, the segmentation process stopped because the length of the segments became smaller than ‘0 . Based on the same procedures mentioned above, the change points for all extreme precipitation indices in the WRB and five sub-regions were calculated; they are summarized in Table 3. Most of the breakpoints occurred in the late 1960s (1965, 1967, 1968 and 1969). Spatially, the upstream experienced more viable climate conditions because change points were detected for all indices: RX1day in 1967, RX3days, R90, R95, R90D, and R95D in 1969; CDD in 1969, 1994, and 2002, and CWD in 1965 and 2003. In addition, CWD has change points in all regions except for the
Jinghe Zone (Table 4), which implies that the CWD has considerably changed during the past decades. The results indicate that the stationarity assumption of the precipitation extremes is invalid for the study region. Notably, the change point of CWD in the upstream in 2003 corresponds to the devastating flood year with total economic losses of up to 0.5 billion dollars (Liang, 2008). This means that the flood disaster could be a response to the non-stationarity of CWD characterized by significant decreasing trends. With respect to other extreme precipitation indices, such as RX1day, RX3days, R90, R95, R95D, and CDD, their non-stationarity in the upstream is consistent with their significant trends. Hence, the stationarity of extreme precipitation indices has been dampened by their significant trends. 4.3. Correlations between the large-scale ocean -atmospheric circulation and extreme precipitation events in the WRB Both PDO and ENSO events show strong relations with extreme precipitation events, as observed worldwide. Investigating the detailed linkages, especially the evolution of the relations, helps us to better understand extreme precipitation variations, which has great implications for precipitation extreme predictions and water resources management. Here, the cross wavelet analysis was applied to study the connections between PDO/ENSO events and RX1day, R90D, and CWD (Figs. 5and 6, respectively), which represent the magnitude, frequency, and duration of precipitation extremes, respectively.
Fig. 4. Segmentations and change points of CDD in the upstream of the WRB. The blue line, the dotted purple line and the solid green line with yellow dots refer to the first, second and third iteration and segmentation process, respectively.
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Table 3 Results of change points of the extreme precipitation indices in the WRB and five sub-regions. Index
RX1day RX3days R90 R95 R90D R95D CDD CWD
Region The WRB
Upstream
Midstream
Downstream
Jinghe
Beiluohe
– – – – – – 1968 1969
1967 1969 1969 1969 1969 1969 1969; 1994; 2002 1965;2003
– – – – – – – 1965;2004
– – – – – – – 1965
2009 – – – – – – –
– – – – – – – 1982
Table 4 Results of the design rainstorm of different return period using the non-stationary RX1day time series and series after change point. Time series type
Non-stationary RX1day Series before the mutation Series after the mutation
Design value Return period (Year) 5
10
50
32.51 39.36 30.21
37.24 47.34 34.19
47.12 67.48 42.42
4.3.1. Teleconnection between PDO and extreme precipitation events Fig. 5 exhibits the cross wavelet transforms between PDO and precipitation extremes in the WRB. Fig. 5A shows that the PDO has a statistically significant positive correlation with the RX1day variations at the 95% confidence level, with a 1–3 year signal from 1981 to 1989 and a 3–5 year signal from 1990 to 2005, which means that PDO has a strong association with changes in extreme precipitation in the WRB. Fig. 5B indicates that the PDO has a statistically significant positive correlation with R90D variations in the WRB at the 95% confidence level, with a 3–5 year signal from 1985 to 1987 and a 2 year signal from 1999 to 2000. Fig. 5C demonstrates that the PDO has a statistically significant positive correlation with CWD variations at the 95% confidence level, with a 3 year signal from 1993 to 1995. These findings suggest that the changes in the frequency and duration of extreme precipitation events in the WRB are significantly linked to the PDO. However, the correlations between PDO and CWD/R90D are relatively weak when compared with those of RX1day for the whole basin and the five sub-regions. Note that the PDO is a long-lived El Niño-like pattern of the Pacific climate variability and the linkages between PDO and precipitation are not consistent with the duration of the PDO. However, a long-periodic series does not mean that its influences on other variables are also long-periodic. As a matter of fact, the time series is composed of many signals with different frequencies (Partal, 2010; Nalley et al., 2012). The advantage of the cross wavelet analysis is to reveal the relationships between two time variables in different time and frequency domains. In addition, some relevant papers (Brownf and Comrie, 2002; Huang et al., 2015, 2016) reported the linkage between PDO and precipitation at 1–3 and 3–5 year intervals, which further verifies the reliability of our findings in this study. 4.3.2. Teleconnection between ENSO and extreme precipitation events The cross wavelet transforms between ENSO events and precipitation extremes in the WRB are displayed in Fig. 6. Obviously, the ENSO events exhibit a statistically negative correlation with RX1day in the WRB with a 3–4 year signal from 1969 to 1971, and positive correlation with 2–5 year signal from 1981 to 1991 and a 3– 6 year signal in 1995–2005 at the 95% confidence level (Fig. 6A). The results indicate that ENSO events played an important role
in triggering intensive precipitation in the WRB. To gain additional insights, we repeated the analysis based on the Pearson and Spearman correlation coefficients. The Pearson and Spearman correlation coefficients between RX1day and MEI from 1960 to 2010 are 0.10 and 0.08, respectively, which are low and insignificant. However, the Pearson and Spearman correlation coefficients of RX1day and MEI from 1969 to 1971 are -0.63 and -0.50, respectively, and those from 1995 to 2005 are 0.34 and 0.55, respectively, which are significant at the 95% confidence level. These significant correlations between the RX1day and MEI series from 1969 to 1971 and 1995 to 2005 were captured by the cross wavelet analysis, demonstrating the importance of considering various time scales when investigating the PDO/ENSO impact on regional precipitation extremes. Similarly, Fig. 6B shows that the ENSO events exhibit a statistically significant positive correlation with R90D variations in the WRB with a 3 year signal at the 95% confidence level from 1966 to 1969 and a 3–5 year signal from 1983 to 1990. This suggests that the ENSO events are closely related to the frequency of extreme precipitation events in this area. Fig. 6C illustrates that the ENSO events show a statistically significant negative correlation with the CWD variations in the WRB with a 1–4 year signal at the 95% confidence level from 1967 to 1971. In addition, ENSO events have a statistically significant positive correlation with the CWD with a 13–14 year signal at the 95% confidence level from 1970 to 1980, implying that the ENSO events are closely associated with longlasting rainy days in the WRB. In addition, the change point of the CWD (Section 4.2) in 1969 in the WRB corresponds to the period when ENSO events are statistically negative correlated with the CWD index from 1967 to 1971. This phenomenon was also observed for the five sub-regions (for brevity, relevant charts are not shown). In general, ENSO events were found to have strong impacts on the nonstationary extreme precipitation series. The comparison of Figs. 5 and 6 shows that the relation between the PDOI and R90D/CWD is not as strong as that between MEI and R90D/CWD. This indicates that the frequency and duration of precipitation extremes in the WRB is influenced more by ENSO events than PDO. This might be attributed to the limitations of the relatively short record length and uncertainty from the potential interaction between PDO and ENSO (Kiem and Verdon-Kidd, 2009). Besides, both PDO and ENSO events have a statistically positive correlation with RX1day from 1981 to 1989 and 1995 to 2005 and R90D from 1985 to 1987. These findings demonstrate that the intensity and frequency of precipitation extremes in the WRB is closely linked to both PDO and ENSO events. We acknowledge that PDO can influence precipitation variability indirectly via modulation of ENSO impacts (Verdon et al., 2004; Kiem and VerdonKidd, 2009). However, the interaction between the PDO and ENSO events cannot be well quantified by the Wavelet analysis method as used in this study. Hence, to what extent ENSO’s impacts on precipitation is modulated by PDO requires further investigations in
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Fig. 5. The cross wavelet transforms between the PDO and precipitation extremes in the WRB: (A) RX1day, (B) R90D and (C) CWD. The thick black contour denotes the relations which are significant at the 95% confidence level against the red noise. The cone of influence (COI) where edge effects might distort the picture is shown as lighter shades. The relative phase relationship is indicated by the arrow direction (with anti-phase pointing left, in-phase pointing right). The color bar on the right denotes the wavelet energy.
the future. We also explored the relationship between the precipitation extremes with other ocean-atmospheric circulation patterns, such as the Atlantic Multi-decadal Oscillation (AMO), but found no evident signal (results not shown).
4.4. Discussion Changes in extreme precipitation events have attracted increasing attention, especially with respect to changing environments.
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Fig. 6. Same as Fig. 5, but for ENSO events: (A) RX1day, (B) R90D and (C) CWD.
However, very few studies investigated the validity of the nonstationarity assumption of precipitation extremes. This paper complements previous studies by focusing on the non-stationarity of extreme precipitation events in the WRB, a typical arid and semiarid climate zone in China. Several change points of the precipitation extremes indices were revealed, implying that the stationarity
assumption of precipitation extremes in the WRB is invalid, especially in the upstream, where change points were detected for all indices. These results are broadly consistent with the observed abrupt changes in precipitation extremes during the 1960s, at 1980, and the 1990s across China (Tu et al., 2010; Chen et al., 2014).
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Fig. 7. Segmentations and change points of annual precipitation in the WRB (A) and in the upstream (B). The blue line refers to the iteration and segmentation process. Since P (tmax) is smaller than the threshold of P0 = 0.95, the segmentation process stops for the first iteration.
Fig. 8. Comparison of the time series before (solid blue line) and after (solid red line) mutation point of RX1day in the upstream. The dashed lines are for the averages of the two time series.
Using the heuristic segmentation method, the stationarity of the annual precipitation in the WRB and upstream were diagnosed and shown in Fig. 7. The blue line in Fig. 7 indicates the iteration and segmentation process; P(tmax) is smaller than the threshold of P0, with 0.9013 in the WRB and 0.9401 in the upstream. The segmentation process stopped, indicating that there was no change point of annual precipitation. This suggests that, compared with the annual precipitation, precipitation extremes were much more sensitive to the changing environments. Therefore, more attention should be paid to extreme precipitation events in the basin, not only because of the tremendous damage by extreme events, but also because of the non-stationarity of the changing environment. To further explore the impact of the non-stationary assumption of the extreme precipitation series, changes in the RX1day series in the upstream are taken as an example, because a change point was detected. Fig. 8 shows the averages of the time series before and after the change point of RX1day in the upstream. The difference
between the two series is notable, with a much smaller mean value after the change point than before. Assuming that the RX1day series follows the Pearson III type curve distribution, we estimated the design precipitation at different return periods using the non-stationary RX1day time series and the series before and after the change point. The results are summarized in Table 4. Table 4 shows that the design value becomes much higher based on the series before the change point, and vice versa based on the series after the change point. The first case might lead to the overinvestment of water projects, whilst the second case might increase the risk of hydraulic work. This means that the assumption of non-stationarity of extreme events could lead to general uncertainties in hydraulic engineering designs and operations. We note that the precipitation intensityfrequency-duration (IFD) relationship, a commonly used method for the design and planning of water supply and management systems worldwide (Wang et al., 2013; Verdon-Kidd and Kiem, 2015),
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is derived based on the assumption of stationarity. With a nonstationary precipitation series, the IFD might result in the failure of design criteria and water management (Verdon-Kidd and Kiem, 2015). Thus, the non-stationarity of extreme precipitation should be explicitly considered and appropriately addressed (VerdonKidd and Kiem, 2015). We emphasize the need to develop a more general non-stationary analysis framework and the importance of modifying relevant guidelines for flood protection design, given the potential of non-stationarity in changing environments (Kiem and Verdon-Kidd, 2013; Madsen et al., 2014).
5. Conclusions The investigation of extreme precipitation events and their changing patterns and causes is of great significance to better understand the regional response of the precipitation to changing environments. In this study, several indices were developed to describe extreme precipitation in the WRB based on which the trends were examined. The non-stationarity of extreme precipitation events was then investigated by examining the changing points of the time series. Subsequently, we studied the linkages between extreme precipitation events and large-scale oceanatmospheric circulation patterns to explore the potential causes behind the variation of precipitation extremes. Generally, no change point was detected in the annual precipitation, while several change points were found in the precipitation extremes. This indicates that extreme precipitation events are much more sensitive to changing environments compared with the annual precipitation. The results imply that assumption of stationary series could lead to severe biases in extreme hydrological frequency analysis, which further affects local/regional hydraulic engineering designs and water resources management. Extreme precipitation events have significant relations with both PDO and ENSO events. This suggests that the large-scale ocean-atmospheric circulation index has the potential to improve the prediction of extreme precipitation in the study region. Notably, ENSO events showed stronger relations with extreme precipitation than PDO. However, caution should be exercised in interpreting the impact of PDO or ENSO on extreme precipitation, given the potential interactions between PDO and ENSO events and other non-climatic factors (e.g., urbanization and topography), which were ignored in this study. This study emphasizes the importance of considering the non-stationarity of extreme series in hydrological frequency analysis. We highlight the asymmetric impact of ENSO/PDO on the frequency, duration, and intensity of precipitation extremes, which has great implications for the prediction and management of extreme hydrological events.
Acknowledgement This research was supported by the National Natural Fund Major Research Plan (91325201), the National Department Public Benefit Research Foundation of Ministry of Water Resources (201501058), the National Major Fundamental Research Program (2011CB403306-2), the National Natural Fund Major Research Plan (51190093), and Project of School of water resources and hydropower of Xi’an University of Technology (2016ZZKT-15).
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