Spatiotemporal Changes in Extreme Precipitation and Its Dependence on Topography over the Poyang Lake Basin, China
Spatiotemporal Changes in Extreme Precipitation and Its Dependence on Topography over the Poyang...
Li, Xianghu;Hu, Qi
2019-02-03 00:00:00
Hindawi Advances in Meteorology Volume 2019, Article ID 1253932, 15 pages https://doi.org/10.1155/2019/1253932 Research Article Spatiotemporal Changes in Extreme Precipitation and Its Dependence on Topography over the Poyang Lake Basin, China 1,2 2 Xianghu Li and Qi Hu Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China School of Natural Resources, Department of Earth and Atmospheric Sciences, University of Nebraska-Lincoln, Lincoln, NE 68583, USA Correspondence should be addressed to Xianghu Li; lxh8010@163.com Received 22 May 2018; Revised 21 December 2018; Accepted 6 January 2019; Published 3 February 2019 Academic Editor: Anthony R. Lupo Copyright © 2019 Xianghu Li and Qi Hu. ,is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Spatiotemporal changes in extreme precipitation at local scales in the context of climate warming are overwhelmingly important for prevention and mitigation of water-related disasters and also provide critical information for effective water resources management. In this study, the variability and trends of extreme precipitation in both time and space in the Poyang Lake basin over the period of 1960–2012 are analyzed. Also, changes in precipitation extremes with topography are investigated, and possible causes are briefly discussed. ,e results show that extreme precipitation over the Poyang Lake basin is intensified during the last 50 years, especially the increasing trends are more significant before the end of the 1990s. Moreover, high contribution rates of extreme precipitation to the total rainfall (40–60%) indicated that extreme precipitation plays an important role to the total water resources in this area. ,e precipitation extremes also exhibited a significant spatial dependence in the basin. ,e northeastern and eastern areas are exposed to high risk of flood disaster with the higher frequency of extreme precipitation events. In addition, the distribution of precipitation extremes had a clear dependence on elevation, and the topography is an important factor affecting the variability of extreme precipitation over the Poyang Lake basin. grid cells show a positive trend during the past 110 years in 1. Introduction South America, Australia, and India, 18% of which are One aspect of the changing climate is change in extreme statistically significant at the 95% confidence level. Similar weather and climate events at regional and global scales changes are also reported in other studies [12, 13]. Tank and [1, 2]. Adverse impacts on the society and environment from Konnen [14] suggested that most of indices for extreme more frequent and severe weather and climate events in the precipitation events have increasing trends in Europe during recent decade have raised concerns of the public, govern- 1946–1999. Hoerling et al. [15] showed that the increases in ments, and academic communities [3]. ,is is especially true heavy precipitation in terms of frequency and intensity have for extreme precipitation events. Mounting evidence is in- been accelerated after 1979 in the northeast United States. dicating that a warming climate could have more frequent Boo et al. [16] reported an increasing in the number of days precipitation extremes [4–8]. and intensity of heavy precipitation over the Korean Pen- Global scale extreme precipitation variability and trend insula. Similar results have also been reported in studies of have shown significant change; the frequency of heavy Arnbjerg-Nielsen et al. [4] and Willems et al. [5]. precipitation events in many areas, particularly in mid- However, because of large differences in regional geo- latitude regions, has increased [9, 10]. For example, Asadieh graphic and atmospheric characteristics, the variability of and Krakauer [11] evaluated trends in global daily pre- precipitation extremes is different across geographical re- cipitation extremes and found that 66.2% of their studied gions [17]. For example, results of variability of precipitation Fuhe river Poyang lake 2 Advances in Meteorology extremes in the Asian Pacific region [18] and Canada [19] 114°E 115°E 116°E 117°E 118°E showed no systematic trends in frequency and intensity of 30°N 30°N Yangtze river extreme precipitation events. Only insignificant increases were found in consecutive wet days in the Arab region, the Greater Horn of Africa [20], and the east Pacific coast of Raohe river Xiushui river 29°N 29°N South America [21]. To the other end, Haylock and Nicholls [22] and Piccarreta et al. [23] have shown that the extreme Xinjiang river precipitation underwent a downward trend in Western Australia and the Mediterranean area from 1951 to 2010. As 28°N 28°N summarized in IPCC [24], increases in the amount of precipitation are at high latitude regions and decreases in many subtropical regions. In addition, Wang et al. [25] showed that the increasing precipitation was more prom- 27°N 27°N inent at higher elevations, while the decrease was more Elevation (m) significant at lower elevation. In China, extreme precipitation events have also exhibited strong regional differences because of the com- 26°N 26°N plexity of topography and climate [26–29]. However, the spatiotemporal heterogeneity of precipitation extremes de- China rived from large-scale datasets or global model outputs is 25°N 25°N coarse or even incorrect at catchment scales. For example, many studies have reported that extreme precipitation events in the past decades have increased in most regions of China [30–34]. Upward trends were found in northwest 114°E 115°E 116°E 117°E 118°E China, southeast coastal regions, and the Yangtze River Rain gauges Subcatchment basin, whereas significant downward trends were found in River Lake central, northern, and northeastern regions of China and Figure 1: Location of study area and the distribution of rain gauges along the Yellow River basin [3, 35–37]. But at local scales, (reproduced from Li and Ye [40], under the creative commons the changing trends of precipitation extremes were more attribution license/public domain). complex and some were not as described by the average trend of a region. For example, precipitation extremes in the middle and lower reaches of the Yangtze River basin show the southward shift of the major warm-season rain band to the statistically significant positive trends [3, 37]. However, no south of the Yangtze River basin. ,is rain band brings more extreme precipitation events to the Poyang Lake basin. ,us, a clear trend has been identified in the upper river basin [36]. Su et al. [38] and Guo et al. [29] pointed out that variations in better understanding of variations in precipitation extremes in extreme precipitation showed distinctively catchment scale the context of climate change in the Poyang Lake basin is vital characteristics and no one pattern fitted in the entire to improve flood prediction and mitigation in this area [47]. Yangtze River basin. ,erefore, quantifying the trends and Among many aspects related to the development of variability in precipitation extremes at the basin and extreme rainfall events is the orographic effect. Certain catchment scale can provide additional and useful in- orographic settings confine the atmospheric circulation and formation for water resources management and mitigation configure intense lifting and favor extreme rainfall. Zhang strategies for flood or drought [39]. et al. [48] suggested that the analysis of changes in pre- In the middle reach of the Yangtze River is the largest cipitation extremes should take into account the orography freshwater lake basin in China, the Poyang Lake basin of a study region. Although the orographic role in severe precipitation has long been recognized, very few of previous (Figure 1) [40]. It has a strategic importance in sustainable development of the economy in southeast China. ,e basin studies have examined such role in the Poyang Lake basin. In is also one of the most frequently flooded areas in China. this study, we extend the previous findings regarding the Frequent severe floods in the last several decades have spatiotemporal changes in precipitation extremes and ex- caused enormous damage to the environment and the amine the role of the orography of the lake basin in pre- economy and threatened the lives of approximately ten cipitation extremes. Our main objectives are (1) to identify million people in the region [41, 42]. Severe flood events in and analyze the trends and variability of extreme pre- the Poyang Lake basin are mainly ascribed to abnormal cipitation events in time and space in the Poyang Lake basin climate condition [43]. For example, in the rainy seasons of during 1960–2012 based on a suite of measures of extreme 1998 and 1954, precipitation was significantly higher than precipitation and (2) to investigate the dependence of the average in the middle Yangtze River basin (with the precipitation extremes on topography in the basin. Out- comes of this study are expected to provide a better un- excessive June-July rainfall of 300 mm and 220 mm, re- spectively) [44]. Hu et al. [45] and Guo et al. [46] also found derstanding of variations in heavy and extreme precipitation that the increase in flood frequency and severity in the events in the Poyang Lake basin for water resource man- Poyang Lake basin in the 1990s was partially attributable to agement and disaster prevention and mitigation. Yangtze river Ganjiang river Advances in Meteorology 3 Table 1: ,reshold of extreme precipitation at different terciles in 2. Study Area and Data the Poyang Lake basin. ,e Poyang Lake basin is located in the middle and lower Percentile (%) 90 95 97 99 reaches of the Yangtze River, China, and the lake receives Precipitation (mm/day) 14.3 25.4 34.6 56.5 water primarily from five subcatchments in its basin (i.e., Xiushui, Ganjiang, Fuhe, Xinjiang, and Raohe) (Fig- 4 2 ure 1). ,e total drainage area of the lake is 16.22 ×10 km , precipitation because the selected terciles have inherent sub- accounting for 9% of the drainage area of the Yangtze River jectivity [58]. According to the precipitation classification basin. ,e topography in the basin is complex, and the suggested by the China Meteorological Administration, an elevation varies from 2200 m (above sea level, asl) in highly event with rainfall exceeding 25.0 mm in a 24-hour period is mountainous regions to about 30 m asl in alluvial plains classified as a heavy precipitation event. ,at rate is close to the downstream of the major watercourses and around the lake. widely used 95th percentile of daily precipitation. ,erefore, the ,e wide alluvial plains surrounding the lake and the broad value of 25.0 mm in a 24-hour period is used as the threshold of alluvial valleys of the tributary streams are important rice- extreme precipitation in the Poyang Lake basin in this study. growing regions in the Jiangxi province [49]. ,e boundary of the basin largely follows the administration boundary of the Jiangxi province, which has a population of 45 million 3.2. Indices of Extreme Precipitation. ,e Expert Team on (in 2012) [50]. ,e Poyang Lake basin has a subtropical wet Climate Change Detection and Indices (ETCCDI) has rec- climate influenced by the East Asian monsoon. ,e mean ommended a list of precipitation extreme indices for use to annual rainfall is 1626 mm for the period of 1960–2012, of estimate precipitation extremes. ,ose indices have been widely which more than 50% falls in March to June, with a peak in used in climate extreme studies [59–61]. In this study, eight June. Annual precipitation in the Poyang Lake basin shows a indices from the list are selected to depict extreme precipitation wet season and a dry season and a short transition period in events in the Poyang Lake basin. ,ese indices can be divided between [49]. ,e water surface area of the lake can exceed 2 2 into three categories: (1) threshold indices, including annual 3000 km in the wet season [42] and shrink to<1000 km extreme precipitation days (EPDs), annual extreme pre- during the dry season [51]. ,e spatial distribution of rainfall cipitation amount (EPA), annual average extreme precipitation in the basin is uneven, with the ratio of maximum to intensity (EPI), and extreme precipitation rate of contribution minimum ranging from 1.65 to 2.51. ,e highest annual (EPR); (2) duration indices, including consecutive dry days rainfall is observed at Wuyuan station (3036 mm) in 1998 (CDDs) and consecutive wet days (CWDs); and (3) absolute and the lowest at Hukou station (776 mm) in 1978. ,e indices, including maximum one-day precipitation amount annual mean temperature is 17.6 C, with an average of (Rx1d) and maximum five-day precipitation amount (Rx5d). ° ° 27.3 C in summer (June–August) and 7.1 C in winter Detailed descriptions of these indices are provided in Table 2. (December–February) [50]. In this study, the observed daily rainfall data at 75 rain gauges in the Poyang Lake basin during the period of 3.3. Trend Analysis. ,e trends of extreme precipitation 1960–2012 as well as the elevation of each rain gauge are events in the Poyang Lake basin are evaluated using the collected from the National Meteorological Information Mann–Kendall (M-K) test [62, 63]. ,e M-K test is a rank- Center of China. ,e locations of those rain gauges are shown based nonparametric method, which is robust against the in Figure 1. Daily rainfall data are used to examine variability influence of extreme data and good for use with biased of precipitation events in the basin. ,e gauge elevation data variables. It has been used for trend detecting in climatologic are used to investigate the relationship of extreme pre- and hydrologic time series [49, 64]. cipitation with elevation and topography. ,e qualities of For any samples of n variables, x , x , x ,. . ., x , the 1 2 3 n these data have been tested, and the data have been widely accumulative number n of samples with x> x (1≤ j≤ i) i i j used in many previous studies [40, 46, 49, 50, 52–54]. should be calculated [49]. A statistical parameter d can be calculated from the following equation: 3. Methods d � n (2≤ k≤ n). (1) k i i�1 3.1. .reshold of Extreme Precipitation Events. Absolute threshold and the percentile methods have often been used Under the null hypothesis of no trend, d is asymptot- to determine extreme precipitation events [55–57]. We use ically normally distributed with expected mean value E(d ) the daily precipitation data from 1960 to 2012 at the 75 and variance Var(d ) as follows: stations and a percentile method to determine the threshold k(k− 1) of extreme precipitation. ,e average threshold of extreme E d � , precipitation at 90, 95, 97, and 99 percentile is 14.3, 25.4, (2) 34.6, and 56.5 mm per day, respectively (Table 1). ,e k(k− 1)(2k + 5) Var d � . threshold of extreme precipitation events determined by the percentile can be different using different percentile points. ,ere is a great deal of debate on which percentile point should With the above assumption, the normalized variable be used to determine the threshold of extreme daily statistic Uf (d ) is calculated from the following equation: k 4 Advances in Meteorology Table 2: Definitions of 8 precipitation extreme indices used in this study. Categories Indices Description Units Annual count of days when daily precipitation≥ the ,e annual extreme precipitation days (EPDs) days threshold of extreme precipitation Annual total precipitation amount when daily ,e annual extreme precipitation amount (EPA) mm ,reshold precipitation≥ the threshold of extreme precipitation indices ,e annual average extreme precipitation intensity EPA divided by EPD mm/day (EPI) Contribution of extreme precipitation to the annual ,e extreme precipitation rate of contribution (EPR) % total rainfall Maximum number of consecutive days with daily ,e consecutive dry days (CDDs) days Duration precipitation< 1 mm indices Maximum number of consecutive days with daily ,e consecutive wet days (CWDs) days precipitation≥ 1 mm Absolute ,e maximum one-day precipitation amount (Rx1d) Annual maximum 1-day precipitation mm indices ,e maximum five-day precipitation amount (Rx5d) Annual maximum consecutive 5-day precipitation mm occurred and the lake was nearly dried up. ,e Rx1d shows a d − E d k k ������� Uf d � , (k � 1, 2, 3, . . . , n), (3) general increase in trend from 1960 to 2012, with the M-K Var d statistic of 0.68 (Table 3). ,e increase is more evident after the mid 1980s, but is not significant in recent years. ,e time where Uf (d ) is the forward sequence, and the backward series of Rx5d also exhibit a positive trend with large sequence Ub(d ) is calculated using the same equation but fluctuations, from 138 mm in 1979 to 289 mm in 1998. It is with a reversed series of data. noted that several peaks such as in 1979, 1998, and 2010 In the M-K test, a positive z value indicates an increasing correspond to the floods in the Poyang Lake with the lake trend and a negative value indicates a decreasing trend. A level exceeding the warning stage by 1.19–3.53 m for 32–93 trend is statistically significant at the 0.05 and 0.01 signifi- consecutive days during that period. cance levels when |z|> 1.96 and 2.576, respectively. 4.2. Spatial Characteristics of Precipitation Extremes. ,e 4. Results spatial distribution of the average values of threshold indices 4.1. Temporal Characteristics of Precipitation Extremes. during 1960–2012 is shown in Figure 4, and the mean values ,e time series of extreme precipitation indices illustrate of extreme precipitation indices in five subcatchments in the how precipitation extremes changed from 1960 to 2012 in Poyang Lake basin are shown in Table 4. It is seen that all the the Poyang Lake basin. ,e variations of the threshold in- threshold indices exhibit large spatial differences across the Poyang Lake basin. For EPD, the low value is found in the dices, including EPD, EPA, EPI, and EPR, in the basin during 1960–2012 are shown in Figure 2. Table 3 shows the western and southern areas where EPD is below 18 days at most stations. High EPD area is in the northeastern and results of corresponding M-K sequential test. It is seen that the EPD ranges from 11 days to 27 days and the EPA ranges eastern basin, where EPD is more than 21 days. ,e spatial between 461 mm and 1313 mm. Both of these indices show a distribution of extreme precipitation amount is similar to long-term increasing trend in the study period with the M-K EPD. Regional average EPA ranges between 920.7 and statistic of 1.27 and 1.42, respectively (Table 3). ,e in- 998.0 mm in Fuhe, Xinjiang, and Raohe subcatchment, but it creasing trend is especially significant before 2000. We also is lower than 827 mm in the Xiushui and Ganjiang sub- note slightly negative changes in the time series of EPD and catchment (Table 4). Some lowest values of EPA are found in EPA in the 2000s. ,e EPI varies between 40.7 mm/day in the south Ganjiang subcatchment. ,e precipitation in- 1963 and 49.3 mm/day in 1998, with a significant trend of tensity is also strong in the northeast, where the EPI has reached up to 47.8 mm/day in the Raohe subcatchment. ,e increase in the entire study period. It is also seen that the EPR ranges from 40 % in 1963 to 60% in 1998 and shows a intensity is weak however in the south with the EPI below 42 mm/day. In addition, the contribution of extreme pre- significant increasing trend at the 0.05 significance level. ,e high values of the EPR indicate that the extreme pre- cipitation to the annual rainfall in the northeastern and cipitation plays a more important role in contributing to the eastern areas is higher than the other areas, with the EPR annual rainfall in the Poyang Lake basin. accounting for more than 52% (Figure 4). In general, the Figure 3 shows the results of the duration and absolute regional averaged values of EPD, EPA, EPI, and EPR in indices, including CDD, CWD, Rx1d, and Rx5d. ,e time Fuhe, Xinjiang, and Raohe subcatchment are higher than series of CDD and CWD exhibit a little change and a slightly that in the Xiushui and Ganjiang subcatchment (Table 4). negative trend in the entire study period, with the M-K Figure 4 and Table 4 indicate that the northeastern and statistic of−0.03 and−1.01, respectively (Table 3). We note eastern areas, i.e., Fuhe, Xinjiang, and Raohe subcatchment, have a higher frequency of extreme precipitation events than that the trend in CDD is dominated by a peak in 1979 and also around the middle 2000s when extreme drought other areas in the Poyang Lake basin. Advances in Meteorology 5 30 1400 y = 2.773x – 4653.9 y = 0.045x – 71.15 R = 0.045 R = 0.032 10 400 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Years Years (a) (b) 50 65 y = 0.037x – 28.63 48 R = 0.076 y = 0.104x – 156.1 40 2 R = 0.115 40 35 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Years Years (c) (d) Figure 2: Variation of (a) EPD, (b) EPA, (c) EPI, and (d) EPR in Poyang Lake basin during 1960–2012. Table 3: Results of M-K test for 8 extreme precipitation indices. ,e spatial patterns of the extreme precipitation indices over the Poyang Lake basin are shown in Figures 6 and 7. ,e Index EPD EPA EPI EPR CDD CWD Rx1d Rx5d percentages of stations showing upward and downward M-K ∗ ∗∗ 1.27 1.42 1.85 2.54 −0.03 −1.01 0.68 0.24 trends for each extreme precipitation index are summarized statistic in Table 5. It is seen that the EPD has increased at 66 (88.0%) ∗ ∗∗ Delineates significance at the 0.1 significance level and delineates sig- of the 75 stations from 1960 to 2012, 7 (9.3%) of which is nificance at the 0.05 significance level. statistically significant at the 0.1 significance level (Table 5). ,e stations with upward trends are distributed fairly Figure 5 shows the spatial distribution of the average uniformly throughout the study area (Figure 6). Only 9 (12.0 duration and absolute indices during 1960–2012. It is seen %) stations have downward trends, none of which is sig- that the CDD exhibits large regional difference, ranging nificant, and these stations are primarily located in the from about 22 days in the western areas to 34 days in the Ganjiang subcatchment. ,e spatial distribution of EPA south. ,e spatial pattern of CWD is different from the trend is similar to EPD trend, with more than 90% of stations CDD, the averaged value of which is 8.9 days in eastern showing an upward trend in the study area. Only 7 (9.3%) and northeastern areas and is 8.3 and 8.7 days in the stations in the southern basin have downward trend (Fig- ure 6). Although the stations showing downward trends for Xiushui and Ganjiang subcatchment, respectively (Table 4). In addition, the absolute indices present a similar spatial EPI increase to 19 (25.3%) mainly in the Ganjiang sub- catchment, 56 (74.7%) stations have upward trends. Among distribution to the threshold indices. Specifically, the Rx1d never exceeds 100 mm in the southern area but reaches to those stations, 9 (12.0%) are statistically significant at the 0.1 140 mm in the northeastern area. Meanwhile, the Rx5d also significance level (Table 5). As for the contribution of ex- ranges from about 150 mm in the south of the Ganjiang treme precipitation, the EPR increases at 69 (92%) stations subcatchment to about 222 mm in the Raohe subcatchment and 24 (32.0%) of which are statistically significant at the 0.1 (Table 4). Figure 5 further indicates that the Fuhe, Xinjiang, significance level (Table 5). Several stations in the southern and Raohe subcatchments are exposed to high risk of flood part of the Ganjiang subcatchment and surrounding region disaster due to the high frequency of extreme precipitation of the lake have downward trends, none of which is sig- events. nificant however (Figure 6). EPI (mm/day) EPD (days) EPA (mm) EPR (%) 6 Advances in Meteorology 60 17 y=–0.0078x + 43.39 y= –0.0078x + 24.28 R = 0.007 R = 0.0003 10 5 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Years Years (a) (b) 140 300 y= 0.098x – 93.89 y= 0.136x – 81.15 R = 0.013 260 R = 0.0036 60 100 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 Years Years (c) (d) Figure 3: Variation of (a) CDD, (b) CWD, (c) Rx1d, and (d) Rx5d in Poyang Lake basin during 1960–2012. EPD (days) EPA (mm) 14.8–16.4 19.8–21.3 639.5–720.0 894.3–977.3 720.0–803.8 16.4–18.2 21.3–23.2 977.3–1141.1 803.8–894.3 18.2–19.8 (a) (b) Figure 4: Continued. Rx1 d (mm/day) CDD (days) Rx5 d (mm/5 days) CWD (days) Advances in Meteorology 7 EPI (mm/day) EPR (%) 41.6–42.6 45.0–46.5 43.3–46.0 50.8–52.5 42.6–43.7 46.0–48.9 46.5–49.6 52.5–55.7 43.7–45.0 48.9–50.8 (c) (d) Figure 4: Spatial distributions of (a) EPD, (b) EPA, (c) EPI, and (d) EPR in Poyang Lake basin. Table 4: Regional average of extreme precipitation indices for five subcatchments. Subcatchment EPD (days) EPA (mm) EPI (mm/day) EPR (%) CDD (days) CWD (days) Rx1d (mm) Rx5d (mm) Xiushui 18.3 826.7 44.9 50.3 27.2 8.3 101.3 189.2 Ganjiang 18.1 789.6 43.5 48.6 28.6 8.7 94.1 174.2 Fuhe 20.4 920.7 44.9 52.0 27.5 8.9 108.2 206.9 Xinjiang 21.5 990.7 45.6 52.4 25.7 8.9 106.3 215.8 Raohe 20.7 998.0 47.8 54.3 27.3 8.9 118.5 222.0 Whole basin 19.0 853.2 44.6 50.26 27.9 8.7 101.0 189.7 one-third of the stations have downward trends, and those Compared to the threshold indices, the variation trends of duration and absolute indices are not as consistent in the stations are mainly found in the Ganjiang subcatchment and Poyang Lake basin. Figure 7 shows that the CDD decreases surrounding areas of the lake (Figure 7). Figures 6 and 7 at 45 (60%) of the 75 stations from 1960 to 2012. Among indicate that the extreme precipitation conditions in the them, only 5 (6.7%) are statistically significant at the 0.1 Poyang Lake basin are aggravating, especially in the significance level (Table 5). ,e remaining 30 (40.0%) sta- northeastern and eastern areas, including Fuhe, Xinjiang, tions have upward trend but none of them is significant. ,e and Raohe subcatchments. spatial trends of CDD suggest that shorter dry spells are situated fairly uniformly throughout the study area, whereas 4.3. Relationship between Precipitation Extremes and upward trends are primarily in the Ganjiang subcatchment (Figure 7). Topography. ,e relationships between extreme pre- cipitation indices at individual rain gauges and elevations in ,e CWD exhibits a similar spatial distribution to that of CDD, but with more stations (59 stations or 78.7%) showing the Poyang Lake basin are shown in Figure 8. It is seen that a downward trend. Some stations with increasing trends are all correlation coefficients (r) between the extreme pre- scattered in the southern part of the Ganjiang subcatchment cipitation indices and elevation have high year-to-year and the surrounding areas of the lake (Figure 7). variability, with r ranging from −0.24 to 0.39 for EPD, For Rx1d and Rx5d, about two-thirds of the stations (48 −0.20 to 0.62 for EPA,−0.35 to 0.60 for EPI,−0.30 to 0.38 for station for Rx1d and 50 stations for Rx5d) display upward EPR,−0.25 to 0.39 for CWD,−0.52 to 0.14 for CDD,−0.25 to trends during 1960–2012. Eleven (14.7%) of the 48 stations 0.74 for Rx1d, and−0.32 to 0.77 for Rx5d. ,is variability is show significant upward trends for Rx1d. ,e remaining mainly associated with the characteristics of precipitation in 8 Advances in Meteorology CDD (days) CWD (days) 22.1–24.9 29.0–31.7 7.5–8.0 8.8–9.3 8.0–8.5 24.9–27.0 31.7–34.7 9.3–10.3 8.5–8.8 27.0–29.0 (a) (b) Rx1d (mm/day) Rx5 d (mm/5 days) 78.5–90.0 108.6–117.7 140.1–159.5 193.2–216.8 90.0–98.8 159.5–175.6 117.7–148.3 216.8–257.6 98.8–108.6 175.6–193.2 (c) (d) Figure 5: Spatial distributions of (a) CDD, (b) CWD, (c) Rx1d, and (d) Rx5d in Poyang Lake basin. different years. In general, among these indices, except the 1984 and 2005. ,ese results indicate that the value of these CDD which has negative correlation with elevation in indices increases as the elevation increases. Figure 8 reveals principle, the others present positive correlation with the that elevation is also an important factor affecting the vari- elevation. ,e correlation coefficients are especially high in ability of precipitation extremes over the Poyang Lake basin. Advances in Meteorology 9 Sig. upward (0.05) Upward trend Sig. upward (0.05) Upward trend Sig. upward (0.1) Downward trend Sig. upward (0.1) Downward trend (a) (b) Sig. upward (0.05) Upward trend Sig. upward (0.05) Upward trend Sig. upward (0.1) Downward trend Sig. upward (0.1) Downward trend (c) (d) Figure 6: Spatial patterns of long-term trends for (a) EPD, (b) EPA, (c) EPI, and (d) EPR in the Poyang Lake basin during 1960–2012. 10 Advances in Meteorology Upward trend Sig.downward (0.1) Upward trend Sig.downward (0.1) Dowward trend Sig.downward (0.5) Dowward trend Sig.downward (0.5) (a) (b) Sig.upward (0.5) Downward trend Upward trend Sig.upward (0.1) Sig.downward (0.1) Downward trend Upward trend (c) (d) Figure 7: Spatial patterns of long-term trends for (a) CDD, (b) CWD, (c) Rx1d, and (d) Rx5d in the Poyang Lake basin during 1960–2012. Advances in Meteorology 11 Table 5: Number and percentage of stations showing upward and downward trends for each extreme precipitation index. Upward trend Downward trend Index Not sig. Sig. at 0.1 Total Not sig. Sig. at 0.1 Total EPD 59 (78.7%) 7 (9.3%) 66 (88.0%) 9 (12.0%) 0 (0) 9 (12.0%) EPA 63 (84.0%) 5 (6.7%) 68 (90.7%) 7 (9.3%) 0 (0) 7 (9.3%) EPI 47 (62.7%) 9 (12.0%) 56 (74.7%) 19 (25.3%) 0 (0) 19 (25.3%) EPR 45 (60.0%) 24 (32.0%) 69 (92.0%) 6 (8.0%) 0 (0) 6 (8.0%) CDD 30 (40.0%) 0 (0) 30 (40.0%) 40 (53.3%) 5 (6.7%) 45 (60.0%) CWD 16 (21.3%) 0 (0) 16 (21.3%) 56 (74.7%) 3 (4.0%) 59 (78.7%) Rx1d 37 (49.3%) 11 (14.7%) 48 (64.0%) 26 (34.7%) 1 (1.3%) 27 (36.0%) Rx5d 50 (66.7%) 0 (0) 50 (66.7%) 25 (33.3%) 0 (0) 25 (33.3%) 1 of stations with downward trend are also largest in the same elevation group. Results in Table 7 indicate that the extreme 0.8 precipitation has an increase trend at stations with elevation 0.6 <100 m or>300 m but a decrease trend at 100–200 m ele- 0.4 vation range. 0.2 –0.2 5. Discussion –0.4 ,e previous sections present the trends and spatial vari- –0.6 1960 1970 1980 1990 2000 2010 ability of extreme precipitation events in the Poyang Lake Years basin. ,e results show that the precipitation extremes are intensified during 1960–2012. In particular, the increasing EPD EPA trends are significant before the year 2000, and high values of EPI EPR CDD CWD EPR indicate that the extreme precipitation played a more Rx1d Rx5 d important role in contributing to the annual rainfall of the Poyang Lake basin. As such, the northeastern and eastern Figure 8: Variation of correlation coefficients between the extreme areas of the basin, including Fuhe, Xinjiang, and Raohe precipitation indices and elevation. subcatchments, are exposed to high risk of floods because of the higher frequency of extreme precipitation events. ,e Table 6 shows the average values of each index in the increasing frequency of precipitation extremes and its un- categorized elevation groups. ,ere are six indices with the even distribution in the lake basin are consistent with the largest value appearing at elevation>300 m. ,e indices of previous studies in the same area [3, 17, 37, 48, 65]. A strong EPA, Rx1d, and Rx5d are much higher in the elevation factor influencing these spatiotemporal distributions in groups, a result indicating that the precipitation extremes are extreme precipitation trends could be the changing climate. According to the analysis of Ye et al. [66] and Zhang et al. intensifying at higher elevation. Generally, the higher alti- tude such as mountains helps increase precipitation, because [67], the mean temperature in the Poyang Lake basin shows topography can change the propagation direction of at- a long-term increasing trend at the rate of 0.1–0.16 C per mospheric moisture and increase rainfall and also reduce decade, due to the intensified global warming and rapid local evaporation of rainfall. industrial development during the last five decades. Zhang ,e statistical relationship between the extreme pre- et al. [65] reported that the intensifying precipitation ex- cipitation trends during 1960–2012 and elevation in the tremes occurred mainly in the north of the Poyang Lake Poyang Lake basin is summarized in Table 7. It is seen that, basin, a finding in agreement with areas dominated by in- for the threshold indices EPD, EPA, EPI, and EPR, over 30 of creasing temperature. ,is increase in extreme precipitation the 40 stations show an upward trend at the group of low events is consistent with expected impacts of climate change elevation (<100 m). Most of the stations also display upward on precipitation, i.e., heavier precipitation resulting from the trend at the middle elevation group (100–200 m and 200– increasing ability of the atmosphere to hold more water as 300 m), although the number of stations with upward trend described by the Clausius–Clapeyron relationship [68]. is less than that in the low elevation group. An intriguing Furthermore, Zou and Ren [69] reported that the East result is that almost all stations show the upward trend at the Asian summer monsoon (EASM) has a significant influence high elevation group (>300 m). Duration indices (CDD and on precipitation extremes, especially for the middle and CWD) exhibit generally an opposite distribution to the lower reaches of the Yangtze River. Many studies have threshold indices. Most stations present a clear downward identified the impacts of EASM on precipitation changes in trend at the elevation group of <100 m and >300 m. Al- eastern China and indicated that the weakening of the EASM though the Rx1d and Rx5d exhibit a similar distribution to after the end of the 1970s remarkably increased summer that of the threshold indices, i.e., the most stations show precipitation in the middle and lower Yangtze River basin upward trend at the elevation group of<100 m, the number [65, 70–72]. ,e Poyang Lake basin is strongly influenced by Correlation coefficient 12 Advances in Meteorology Table 6: Average values of extreme precipitation indices in the categorized elevation groups. Elevation (m) EPD (days) EPA (mm) EPI (mm/day) EPR (%) CDD (days) CWD (days) Rx1d (mm) Rx5d (mm) <50 18.0 824.6 45.4 51.2 28.6 8.2 104.4 189.8 50–100 19.6 886.8 44.9 50.8 27.0 8.7 102.9 198.0 100–150 18.5 813.6 43.8 48.8 27.7 8.8 95.2 181.1 150–200 18.1 784.0 43.2 48.6 29.0 8.6 93.1 175.0 200–250 19.8 883.2 44.2 51.1 30.3 9.1 102.7 188.1 250–300 18.8 830.0 43.9 48.7 27.4 8.9 95.7 184.0 >300 20.5 946.4 45.5 50.6 26.1 9.4 118.4 207.3 Table 7: Distribution of stations showing upward and downward trends in the categorized elevation groups. Elevation group (m) Number of stations Trend EPD EPA EPI EPR CDD CWD Rx1d Rx5d Upward 38 40 30 38 11 5 24 22 <100 40 Downward 2 0 10 2 29 35 16 18 Upward 17 18 14 19 11 9 14 18 100–200 22 Downward 5 4 8 3 11 13 8 4 Upward 9 9 10 9 8 3 7 8 200–300 10 Downward 1 1 0 1 2 7 3 2 Upward 3 3 2 3 1 0 3 3 >300 3 Downward 0 0 1 0 2 3 0 0 patterns in summer over China are connected with the the EASM. When a larger amount of atmospheric moisture is transported from East or South China Sea by the monsoon interannual changes of the summer NAO. Xiao et al. [72] also found that the negative PDO event at the same year circulation, there is likely to have more heavy rainfall [3]. Weakening in the EASM would limit its northward ex- tends to increase the occurrence of precipitation events in tension and keep a longer rainy season in southern China. spring, and both the occurrence and intensity of pre- ,is position favors a steady increase in not only the total cipitation events during summer and autumn months have rainfall but also the extreme precipitation events in summer been influenced by the ENSO and IOD. Xiao et al. [72] in the Poyang Lake basin [36, 56, 73]. Hu et al. [45] and Guo further pointed out that the influences of ENSO, NAO, IOD, et al. [46] also showed that the increase in heavy and extreme and PDO on the seasonal occurrence and intensity of precipitation events in the Poyang Lake basin in the 1990s precipitation events are complex. was partially attributable to the southward shift of warm- In addition, this study also shows that the extreme precipitation indices had strong positive correlations with season rain bands to the south of the Yangtze River basin. ,e El Niño-Southern Oscillation (ENSO) event is an- regional features of terrain elevation. ,is finding is in agreement with the results of Zhang et al. [48], who showed other factor that may contribute to the changes and trends of extreme precipitation in this region. Recent studies of ENSO that higher elevations in the Poyang Lake basin often co- have identified a remarkable climatic link between extreme incide with higher Rx1d in summer and autumn. Zhang and precipitation and both the warm (El Niño) and cold (La Liu [79] also found that Rx1d and Rx5d are generally Niña) phases of Southern Oscillation in various areas of the positively correlated with the mean elevation and slope rate, world [65, 72, 74]. Duan et al. [75] found that during the in the adjacent region of the Poyang Lake basin (the rainy season in southern China, warm ENSO episodes can Dongting Lake basin). Similar results are also reported in promote the convergence of the water vapor flux, boosting studies by Wang et al. [25], Guan et al. [17], and Li et al. [80]. the moisture content and hence the likelihood of extreme However, Zhang et al. [48] further pointed out that the influences of orography on changes of extreme precipitation precipitation events. Zhang et al. [48] revealed that the correlation between the absolute indices Rx1d and Rx5d and are complicated. ,erefore, further work needs to be carried ENSO is statistically positive in the Poyang Lake basin, and out in the future to address more detailed assessments of the they are influenced mainly by the ENSO events with a one- interrelationship between topography and extreme pre- year time lag. ,ey further showed that the occurrence of cipitation variation trends. ENSO can amplify the variability of extreme precipitation in summer [48]. Shankman et al. [42] also found that the most 6. Conclusions severe floods in the Poyang Lake since 1950 occurred during or immediately following El Niño events. ,e extreme ,is study used a suite of extreme precipitation-related precipitation in the Poyang Lake basin is also been influ- indices and analyzed the trend and variability of extreme enced by the North Atlantic Oscillation (NAO), Indian precipitation from 1960 to 2012 in the Poyang Lake basin Ocean Dipole (IOD), and Pacific Decadal Oscillation (PDO) and the relationship of extreme precipitation with basin’s [76–78]. Linderholm et al. [76] have revealed that the climate elevation and orography. Results show that extreme Advances in Meteorology 13 precipitation in the Poyang Lake basin intensified during References the last five decades. Except for CDD and CWD which [1] S. Rahmstorf and D. 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Advances in Meteorology
Hindawi Publishing Corporation
http://www.deepdyve.com/lp/hindawi-publishing-corporation/spatiotemporal-changes-in-extreme-precipitation-and-its-dependence-on-2HeLsm03xR