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In the context of global warming, with the increasing frequency of occurrence of extreme events worldwide, much attention has been paid to changes in extreme weather, with one focus being on extreme precipitation. Numerous studies have provided evidence for increases in the frequency and magnitude of extreme precipitation events in various parts of the world (Alexander et al., 2006), including America, Europe, and China (Karl and Knight, 1998; Klein Tank and Können, 2003; Zhai et al., 2005; Trenberth et al., 2007).
Early studies on extreme precipitation events paid close attention to single-station daily events (Zhang et al., 2000; Zhai et al., 2005; Klein Tank et al., 2006; Wang et al., 2007). The IPCC Special Report on Extremes (SREX) (IPCC, 2012) assessed the complex relationship between disasters and extreme events including extreme precipitation events, which depends on exposure and vulnerability as well as the severity of the extreme event itself. The spatial extent and the temporal persistence of an extreme event are two important aspects of its character and defining them is therefore important. For more than ten years, regional extreme precipitation, which includes the spatial extent and the temporal persistence, has attracted a growing concern (Moberg and Jones, 2005; Zhai et al., 2005; Xu et al., 2011; Kunkel et al., 2013; Walsh et al., 2014; Wu, 2015; Zou and Ren, 2015; Niu et al., 2018). In the context of research methods to define large-scale continuous regional extreme precipitation events, Ren et al. (2018) defined three categories: (1) Spatial simultaneity analyses, focusing on the impact area of events; (2) temporal persistence, emphasizing the study of time series; (3) identifying regional extreme events, taking both spatial continuity and temporal persistence into account.
Chen and Zhai (2013) identified regional heavy precipitation events by first identifying the continuity of a single station and then considering the spatial consistency. Ren et al. (2012) defined the acronym Regional Heavy Precipitation Events (RHPEs), which are identified through the general objective identification technique for regional extreme events (OITREE). Using this framework, Zou and Ren (2015) analyzed the spatio-temporal characteristics of RHPEs in China from 1961 to 2012 using the OITREE (These and other acronyms used in this paper are shown in Table 1).
Acronym Full Name Purpose Reference OSAT Objective Synoptic Analysis Technique Objective technique to identify which rainfall observations are associated with the eye and the rain belts of a tropical cyclone Ren et al., 2007 TCP Tropical Cyclone Precipitation The precipitation associated with a tropical cyclone as identified
by the objective OSAT techniqueRen et al., 2007 TPE Tropical Cyclone Event A number of consecutive days containing tropical
cyclone precipitation (TCP) over landThis study OITREE Objective Identification Technique for Regional Extreme Events Objective technique to define extreme events in rainfall, temperature or other parameters occurring over a region and a period of several days based on extreme values of defined indices. Ren et al., 2012 RHPE Regional Heavy Precipitation Event Extreme rainfall events as defined by the OITREE technique Zou and Ren, 2015 TRHPE Typhoon Regional Heavy Precipitation Event A regional heavy precipitation event (RHPE) that overlaps in space and time with a tropical cyclone event (TPE) This study Table 1. The acronyms used in this study to describe precipitation events and the objective algorithms used in their definition.
Most daily precipitation records, worldwide and in China are associated with tropical cyclones or typhoons (Tao, 1980). For example, Reunion Island in the Southern Indian Ocean set a record for the highest precipitation in March 1952, with the maximum rainfall reaching 1870 mm in 24 hours, brought about by the presence of a tropical cyclone (Chen, 1977). In August 1975, a typhoon named Nina caused an extreme rainstorm of 1062 mm in 24 hours (Ding, 1994) at Zhumadian in central China, this being a record for the Chinese mainland. Generally, heavy rainfall associated with a typhoon cyclone often leads to disasters such as reservoir collapse, landslide, debris flow and flash flood, posing a threat to life and society.
Given this, it is of interest to determine the contribution of tropical cyclones to RHPEs. Changes in Tropical Cyclone Precipitation (TCP) have been documented by Ren et al. (2002, 2006), Cheng et al. (2007), Wang et al. (2008), Knight and Davis (2007, 2009), Jiang and Zipser (2010), and Lavender and McBride (2021). Some studies have analyzed the extreme precipitation of tropical cyclones from the perspective of extremes at individual stations (Knight and Davis, 2009; Jiang and Qi, 2016; Villarini and Denniston, 2016; Jiang et al., 2018; Cai et al., 2019; Dhakal, 2019; Qiu et al., 2019). Some studies have also analyzed the involved possible mechanisms of TCP, such as TC translation speeds (Lai et al., 2020), average duration and intensity of strong TCs (Liu and Wang, 2020), and atmospheric water vapor content and moisture transport (Gao et al., 2021). Although tropical cyclones produce the most intense rainfall in the world (Tao, 1980), few studies pay attention to RHPEs in the context of tropical cyclones.
In this study focusing on typhoon regional heavy precipitation events in China, we examine the contribution of tropical cyclones to RHPEs. Thus, we develop an objective definition of combined events, referred to here as Typhoon Regional Heavy Precipitation Events (TRHPEs). Section 2 introduces the data and methods. Section 3 outlines the objective methodology to identify TRHPEs. The main section of the paper is section 4 which uses station rainfall data over China in the period 1960−2018 to identify 86 TRHPEs. Their spatial structure over China, the seasonal distribution, and their percentage contribution to RHPEs are analyzed, along with temporal changes and relationship to cyclone tracks and speed of movement. A summary and concluding remarks are provided in section 5.
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Using the objective method described in Section 3, a total of 86 TRHPEs were identified. Based on the highest values of the integrated index Z, the top 10 TRHPEs are shown in Table 2. The No. 1 TRHPE is associated with Typhoon Herb in 1996, with the precipitation covering Southeast China, the middle and Lower Yangtze River and Taiwan. The extreme intensity 1094.5 mm occurred over Taiwan and is one of the most extreme daily totals ever recorded in China. The second most severe TRHPE is associated with Typhoon Tim (1994), which landed in Taiwan on July 10 and then moved northwestward, affecting Southeast China and Central China successively (Meng et al., 2002). It should be noticed that most of the RHPEs continued after the typhoon dissipated, which indicates an important contribution of post-landfall cyclone remnants to extreme regional rainfall events.
No. Year Ranking of RHPEs Typhoon RHPE duration TPE duration Influence area integrated index Z Extreme
intensity (mm)1 1996 7 Herb 7.31−8.7 7.30−8.4 Southeast China and Middle and Lower Yangtze River 7.88 1094.5 2 1994 11 Tim 7.10−7.16 7.9−7.13 Southeast China and Central China 6.44 538.7 3 1994 12 Caitlin 8.4−8.10 8.2−8.5 Southeast China 6.39 538.7 4 1982 17 Andy 7.30−8.8 7.28−8.2 Southeast China and Middle and Lower Yangtze River 5.99 440.5 5 1969 18 Betty 8.8−8.15 8.7−8.11 Southeast China and Central China 5.9 264.7 6 2001 21 Toraji 7.25−8.1 7.29−8.2 Southeast China and East China 5.68 337.7 7 1964 31 Ida 8.9−8.16 8.6−8.10 South China 5.4 244.4 8 2007 38 Sepat 8.18−8.27 8.16−8.24 Southeast China and Middle and Lower Yangtze River 5.24 281.4 9 1994 53 Russ 6.6−6.11 6.3−6.11 South China 4.91 357 10 2008 68 Kammuri 8.6−8.12 8.3−8.8 South China 4.65 523.5 Table 2. List of top 10 TRHPEs over China
Inspection of the 4th and 5th columns of the Table reveals that the RHPE almost always continues for several days after the TPE has been completed. For the leading ten events, the number of days the RHPE continued after the TPE finished are respectively (3, 3, 5, 6, 4, −1, 6, 3, 0, 4). This is one of the first findings of this study: the post landfall rainfall is a major contributor to the RHPEs.
Figure 4 shows TRHPE frequency (blue line, right hand ordinate) for different extreme intensity values. The columns (left hand ordinate) show the ratio of the number of TRHPEs to RHPEs (orange column) and to TPEs (grey column). The extreme intensity of TRHPEs is mainly between 200 and 600 mm, with 48 events between 200−400 mm and 27 events between 400−600 mm. The ratios of TRHPEs to the two original precipitation events increase with increasing extreme precipitation intensity. For single station extreme intensities (
$ {I}_{1} $ ) equal to or greater than 1000 mm intensity, the ratio of TRHPEs to TPEs reaches 100%. This is consistent with the conclusion of Chen et al. (2010) that the most serious extreme precipitation in China is related to typhoons. It is also noted that most TPEs are not TRHPEs, the ratio being only 8%.Figure 4. TRHPE frequency (blue line, right hand ordinate) for different extreme intensity values. The extreme intensity values displayed on the abscissa are the highest daily rainfall at a single station during the event. The columns (left hand ordinate) show the ratio of the number of TRHPEs to RHPEs (orange column) and to TPEs (grey column).
Figure 5a displays the annual variation of TRHPEs during 1960−2018 with an upward trend at a rate of 0.14 (10 yr)-1, significant only at the 10% level according to the non-parametric Kendall’s tau test (Kendall and Gibbons, 1981) (All the trend analyses in this paper are based on this method). The maximum annual frequency is 5 in 1994. Over the period of the 59 years, there have been 12 years without TRHPEs, including four in the 1970s and three in the 1980s. The trend of RHPE annual frequency (Fig. 5b) is also positive, consistent with TRHPE’s. The trend of TPEs (Fig. 5c) is downwards, consistent with the finding of Wang et al. (2008), with a long-term downward trend at a rate of −0.88 (10 yr)−1, which is statistically significant at a level of 0.05. As discussed by Wang et al. (2008), this long-term decrease in tropical cyclone rainfall over China is related to a shift in tropical cyclone tracks, specifically with fewer landfalls occurring over Southern China.
Figure 5. Annual variations of (a) TRHPE frequency, (b) RHPE frequency, (c)TPE frequency, (d) ratio of TRHPEs to RHPEs, and (e) ratio of TRHPEs to TPEs (the dotted line represents the linear trend).
The peak value of the annual ratio of TRHPEs to RHPEs (Fig. 5d) and TRHPE’s to TPEs (Fig. 5e) basically corresponds to the peak value of the annual TRHPE frequency, with a maximum in 1994. They all display an increasing trend from 1960 to 2018. The ratio of TRHPEs to TPEs has an upward trend at a rate of 0.14% (10 yr)−1 which is statistically significant at the 5% confidence level.
Variations of the annual accumulated integrated index Z, and the five single indices for TRHPEs of China during 1960−2018 do not have apparent trends and all fail to pass the significance test, so the related figures are omitted. The annual extreme intensity has a weak increasing trend with a rate of 3.8 mm (10 yr)−1. They all had high values during the middle 1960s and the middle and late 1990s.
It is noted in Fig. 5d that the TRHPEs contribute only of the order of 20% to the RHPEs, consistent with 86 TRHPEs out of 454 RHPEs over the 59 years. This small percentage is mainly due to the fact that the RHPEs occur over all of China, as shown in the spatial distribution climatology of RHPE’s of Zou and Ren (2015), and that a large percentage of the RHPEs are associated with the Mei-Yu front. The seasonal variations of TRHPE frequency and extreme intensity are shown in Fig. 6. TRHPEs occur from May to October, with a maximum of 43 events in August followed by July with 24 events (Fig. 6a), consistent with the conclusion of Jiang and Qi (2016) that TC extreme precipitation occurs most frequently in July and August. Comparing Fig. 6a with the distribution of RHPEs in Fig. 6 of Zou and Ren (2015), it is seen that the distribution of TRHPEs lags that of RHPEs, which have high frequencies in June and July.
Figure 6. Seasonal variations of TRHPEs over China during 1960−2018: (a) frequency; (b) maximum extreme intensity (mm).
Consistent with the frequency distribution, August is also the peak of the monthly maximum extreme intensity of TRHPEs, followed by July and October, with maximum extreme intensity exceeding 1000 mm (Fig. 6b). The frequency and extreme intensity of TRHPEs have seasonal characteristics, with a unimodal pattern in frequency and a bimodal pattern in intensity, though the apparent bimodal structure may possibly be due to sampling limitations. TRHPEs are prevalent in summer and have a high precipitation intensity, peaking in August, and rarely occur in spring and winter, which is consistent with the activities of tropical cyclones affecting China (Ren et al., 2011).
The spatial distribution of the frequency of TRHPEs is presented in Fig. 7a, showing that TRHPEs influence most of central and eastern China, especially the coastal regions, with a decrease in frequency from southeast to northwest. High-frequency values mainly affect southwest Zhejiang and coastal areas of Fujian with more than 30 events over the 50 years. Typically, less than 10 events influence northern China. Accumulative intensity shows high values above 3000 mm over coastal areas in Southeast and South China. The spatial distribution of accumulated intensity is consistent with that of frequency, with a decrease from southeast to northwest (Fig. 7b). It is noted that high frequency and accumulative intensity area spread from southeast to south China, which may be related to the westward movement of the prevailing typhoon track first proposed by Wu et al. (2005).
Figure 7. Spatial distribution of TRHPE frequency and accumulated intensity (
$ {I}_{2} $ , units: mm) over China. Frequency/accumulated intensity: (a/b) during 1960−2018, (c/d) the difference between 1990−2018 and 1960−1989 (later period minus earlier period).As shown in Figs.7c and 7d, the frequency and accumulative intensity during 1990−2018 are both higher than 1960−89, especially in some of the coastal areas of Southeast and South China. Frequency increases by 17 events in Xiamen, and accumulative intensity increases by more than 4000 mm in Alishan. This spatial distribution of the influence of the TRHPE is consistent with the earlier findings of Luo et al. (2016) on the contributions of TC events to extreme station precipitation over China.
To further analyze the increase in TRHPE frequency and accumulative intensity between the earlier and the later period, a mean intensity index for a specific type of event is defined:
where n is the number of events in a year, and mi is the number of occurrences of single-station daily precipitation exceeding ≥100 mm or ≥250 mm during the event. MII is defined only when the n, the number of events, is non-zero.
As shown in Fig. 8, the mean intensity indices (MIIs) in tropical cyclone events (TPEs) have positive trends for both indices ≥100 mm and ≥250 mm, significant at the 95% confidence level. It is noted that MII also has positive trends at both 100 mm and 250 mm for general RHPE events, though not significant at the 95% level, (figures omitted). The increase in intensity of TPE events (as quantified by the MII indices) is consistent with the positive trends shown earlier in Fig. 5 for both the number of TRHPE events (Fig. 5a) and the percentage of tropical cyclone events that are classified as TRHPE events (Fig. 5e). So, it is reasonable to believe that the increasing trends in MIIs in TPEs have resulted in the insignificant upward trend of TRHPEs during 1960−2018.
Figure 8. Temporal variations of Mean Intensity Indices (MIIs) for tropical cyclone precipitation events (TPE) of grade (a) ≥100 mm, (b) ≥250 mm for the period 1960−2018.
To explore the possible roles of internal Tropical Cyclone variables on the characteristics of the TRHPEs, correlations were performed against seven indices of the TRHPEs, including the integrated index Z, the five single indices and the volume precipitation. Table 3 shows the correlations that were statistically significant. TRHPE extreme intensity is positively correlated with average TC intensity and has a negative correlation with the minimum cyclone translation speed. The correlations in the table make physical sense in that the slower the TC translation speed, the greater the integrated precipitation and the longer the TRHPE event duration. However, despite the statistical significance, the correlations are small. Trends were also examined for the internal TC variables, but no significant trends were found. Kossin (2018) indicated that the annual mean tropical cyclone translation speed shows significant slowdown trends over both land and ocean in the basin of the western North Pacific. However, our analysis for TPE events affecting China finds no significant long-term trend for either average TRHPE translation speed or minimum TRHPE translation speed.
Volume precipitation Extreme intensity Duration Average TRHPE translation speed −0.083 −0.106 −0.303* Minimum TRHPE translation speed −0.209* −0.187 −0125 Average TRHPE wind speed 0.037 0.267* −0.037 Table 3. Correlation coefficients between internal variables of TCs (rows) and indices of the TRHPE (columns). The first two rows are the average and minimum TC translation speed during the TRHPE event. The third row is average TC intensity as measured by maximum wind speed during the TRHPE. The three columns are the precipitation volume, the maximum single-station daily rainfall intensity and the duration of the TRHPE event. Bold numbers are significant at the 90% level, with * at the 95% level, using non-parametric Kendall tau test.
A more promising line of investigation to understand the temporal changes shown in Fig.7 is the analysis of the variation in cyclone tracks between the earlier and the later period. Figure 9a shows that the number of TC tracks causing TRHPEs is 34 during 1960 to 1989 and 52 during 1990 to 2018 (Fig. 9b). The TRHPE TCs can be divided into two categories according to the tracks—the turning-track TCs landing on Southeast China and the westward-track TCs landing on South China. Inspection of the figures shows the number of TCs in the first category is greater than in the second category, which means that Southeast China is more frequently affected by TRHPEs than South China. In addition, the number of the first category of TCs increases from 11 in 1960−80 to 17 in 1990−2010, and that of the second category of TCs increases from 17 to 24.
Acronym | Full Name | Purpose | Reference |
OSAT | Objective Synoptic Analysis Technique | Objective technique to identify which rainfall observations are associated with the eye and the rain belts of a tropical cyclone | Ren et al., 2007 |
TCP | Tropical Cyclone Precipitation | The precipitation associated with a tropical cyclone as identified by the objective OSAT technique | Ren et al., 2007 |
TPE | Tropical Cyclone Event | A number of consecutive days containing tropical cyclone precipitation (TCP) over land | This study |
OITREE | Objective Identification Technique for Regional Extreme Events | Objective technique to define extreme events in rainfall, temperature or other parameters occurring over a region and a period of several days based on extreme values of defined indices. | Ren et al., 2012 |
RHPE | Regional Heavy Precipitation Event | Extreme rainfall events as defined by the OITREE technique | Zou and Ren, 2015 |
TRHPE | Typhoon Regional Heavy Precipitation Event | A regional heavy precipitation event (RHPE) that overlaps in space and time with a tropical cyclone event (TPE) | This study |