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Dataset Profile Dataset title A potential risk index dataset for landfalling tropical cyclones over the Chinese mainland, Version 1.0 (PRITC dataset V1.0) Time range 1949–2018 Geographical scope
Data formatThe Chinese mainland
TextData volume 68 KB Data service system http://tcdata.typhoon.org.cn/en/qzfxzs_sm.html Sources of funding National Key Research and Development Program of China (2017YFC1501604), National Natural Science Foundations of China (41875114), Shanghai Science & Technology Research Program (19dz1200101), National Basic Research Program of China (2015CB452806), and Basic Research Projects of the Shanghai Typhoon Institute of the China Meteorological Administration (2020JB06).
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The dataset has one file in text format; the name of this file is “PRITC_V1.0.txt”. The file has two parts: the headline and the other data lines. The data lines have 10 columns as defined by the corresponding column in the headline. The data of TCs that made landfall in the 2017 season were chosen as examples with which to describe the organization of the data lines (Table 1), and a list of the abbreviations used in the dataset is shown in Table 2.
SN YEAR ID TCNAME PRO_LD CAT_LD IPT IWT IPWT CAT_IPWT 564 2017 20170302 MERBOK Guangdong STS 0.1314 0.0647 0.1023 1 565 2017 20170807 ROKE Hongkong TS 0.0254 0.0426 0.0345 1 566 2017 20171009 NESAT Fujian TY 0.275 0.1215 0.215 2 567 2017 20171110 HAITANG Fujian TS 0.4197 0.1505 0.3167 3 568 2017 20171413 HATO Guangdong STY 0.3872 0.4214 0.4859 4 569 2017 20171514 PAKHAR Guangdong STS 0.1755 0.151 0.1765 2 570 2017 20171716 MAWAR Guangdong TS 0.0763 0.0304 0.0545 1 571 2017 20172100 (nameless) Hainan TS 0.0045 0.0156 0.0101 0 572 2017 20172320 KHANUN Guangdong STS 0.1621 0.2046 0.1999 2 Table 1. Sample data from the potential risk index dataset for landfalling tropical cyclones over the Chinese mainland (PRITC dataset V1.0).
Abbreviation Description SN The serial number starting from 1. YEAR The year of the corresponding TC. ID A unique identifier for the TC, containing the information of the year, TC serial number, and Chinese TC serial number. TCNAME TC name. “(nameless)” means an unnamed TC. PRO_LD The province where the TC first made landfall on the Chinese mainland. CAT_LD The TC’s intensity grade at the time of its first landfall over the Chinese mainland. IPT The index of TC-induced precipitation calculated with Eq. (3). IWT The index of TC-induced wind calculated with Eq. (1). IPWT The combined index of TC-induced precipitation calculated with Eq. (5). CAT_IPWT The category level ranging from 0 to 4 based on IPWT. >=TY Indicates that the TC landfall intensity grade was equal to or stronger than a typhoon during 1949 to 1972. NTC Indicates storms that were weaker than tropical depressions and had maximum wind speeds lower than 10.8 m s−1
Chen et al., 2016.Table 2. List of abbreviations used in the potential risk indices dataset.
The headline is also shown in Table 1 and includes the following 10 items.
SN is the serial number starting from 1.
YEAR is the year of the corresponding TC.
ID is a unique identifier for each TC. The format of the ID is “YYYYaabb”. “YYYY” is the four-digit year. “aa” is the two-digit TC serial number. “bb” is the two-digit Chinese TC serial number. The values “00” indicate a lack of Chinese TC serial numbers for all TCs before 1959 or means that the object is an unnamed TC that occurred after 2000. The TC serial numbers and the Chinese TC serial numbers were obtained from CMA’s TC database. For example, “20171413” means that the TC was the 14th TC and the 13th named TC in the 2017 season, referring to Hato (2017).
TCNAME is the name of the corresponding TC. The name of an unnamed TC is replaced by “(nameless)” in the corresponding column.
PRO_LD is the province where the corresponding TC first made landfall on the Chinese mainland. Some TCs reenter the sea after moving over land for some time and make second or even third landfalls.
CAT_LD is the TC’s intensity grade at the time of its first landfall over the Chinese mainland. There are six standard grades including a super typhoon (SuperTY), severe typhoon (STY), typhoon (TY), severe tropical storm (STS), tropical storm (TS), and tropical depression (TD), according to the national standard of China ICS 07.060 (GB/T 19201-2006); a grade called a nontropical cyclone (NTC) was added for storms that were weaker than TDs and had maximum wind speeds lower than 10.8 m s−1 (Chen et al., 2016). The term “>=TY” indicates that the intensity grade was equal to or stronger than TY because the maximum TC landfall intensity grade recorded at the TC landfall was grade 12 based on the Beaufort scale; wind speed records were missing from 1949 to 1972.
IWT, IPT, and IPWT were calculated with Eqs. (1), (3), and (5), respectively. According to Table 1, the IPT, IWT, and IPWT values of Hato (2017) were 0.3872, 0.4214, and 0.4859, respectively.
CAT_IPWT is the category of IPWT as defined in Table 3. CAT_IPWT was assigned five categories (from 0 to 4) with increasing impact severities and was determined based on the corresponding relation between IPWT, the damage index, and the categories based on typhoon cases from 1984 to 2013. Note that the five categories were not based on equal bin sizes of the IPWT values but were based on a consideration of the proportion of the damaging typhoons in each category. During this period, 98.6%, 87.7%, 72.9%, 69.8%, and 73.4% of typhoon cases were members of the same damage category or the next damage category down (i.e., CAT_IPWT Categories 0+1, 1+2, 2+3, and 3+4, respectively (Chen et al., 2019, see their Fig. 6). The CAT_IPWT value of Hato (2017) was 4, implying that it was one of the most destructive TCs.
CAT_IPWT Meaning IPWT range 0 Tiny impact <0.0234 1 Mild impact [0.0234,0.1480) 2 Medium impact [0.1480,0.2570) 3 Severe impact [0.2570,0.4200) 4 Super severe impact ≥0.4200 Table 3. The classification basis and means of CAT_IPWT. The “[” symbols in the table indicate the inclusion of the value, and the “)” symbols indicate that the value is left out. For example, 0.0234 ≤ IPWT < 0.1480 is shown as “[0.0234, 0.1480)” (Chen et al., 2019).
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As shown in Fig. 2, most (96.5%) of the 1401 meteorological stations used in calculating the indices were built step-by-step before 1960. Thus, the representativeness of the calculated indices was more reliable after 1960, and the basic statistics of the indices were given for the period between 1960 and 2018 in this section. There were 486 TC cases in the period.
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The frequency distributions of IPWT, IWT, and IPT and boxplots of the TCs are shown in Fig. 3. The means (medians) of the IPT, IWT, and IPWT values were 0.2321 (0.1951), 0.1414 (0.1075), and 0.2109 (0.1620), respectively. Usually, IWT was smaller than IPT and IPWT.
Figure 3. Box plots of the IPT, IWT, and IPWT values of TCs from 1960 to 2018. “X” indicates the mean. The bottom, central, and top of the line of each box are the 25%, 50%, and 75% values, respectively.
The top 10 IPT, IWT, and IPWT values are listed in Table 4, and these TC tracks are shown in Fig. 4. The highest IPT, IWT, and IPWT values were 1.0, 1.0, and 1.2837, respectively. Among the top 10 TCs ranked by the IPWT, 6 made landfall over the coastal areas of Fujian Province and 3 made landfall over Guangdong Province (Fig. 4).
Rank IPWT IPT IWT TC name IPWT TC name IPT TC name IWT 1 Winnie (1997) 1.2837 Bilis (2006) 1.0000 Winnie (1997) 1.0000 2 Bilis (2006) 1.1090 Mary (1960) 0.8537 Babe (1977) 0.6936 3 Mangkhut (2018) 0.9850 Amy (1962) 0.8444 Matsa (2005) 0.6873 4 Amy (1962) 0.9538 Mangkhut (2018) 0.7870 Mangkhut (2018) 0.6617 5 Freda (1984) 0.7930 Winnie (1997) 0.7834 Andy (1982) 0.6405 6 Polly (1992) 0.7490 Herb (1996) 0.7647 Bilis (2006) 0.609 7 Mary (1960) 0.7431 Vongfong (2002) 0.7461 Opal (1962) 0.5883 8 Herb (1996) 0.7289 Usagi (2013) 0.7249 Amy (1962) 0.5764 9 Prapiroon (2006) 0.6784 Polly (1992) 0.7100 Hope (1979) 0.5744 10 Tim (1994) 0.6742 June (1984) 0.7096 Abe (1990) 0.5416 Table 4. The top 10 TCs that occurred between 1960 and 2018 ranked by IPWT, IPT, or IWT.
Figure 4. The tracks of the top 10 TCs as ranked by IPWT from 1960 to 2018. The number beside each TC name indicates its ranking. For example, “1. Winnie” means that Winnie was the TC with the largest IPWT. “GD”, “FJ”, and “ZJ” are the locations of Guangdong, Fujian, and Zhejiang Provinces, respectively.
The TC with the most destructive potential as indicated by IPWT was Winnie (1997), which also had the highest IWT value and the 7th highest IPT value. This TC made landfall on Wenling city, Zhejiang Province, with a landfall intensity of 40 m s−1. It brought heavy precipitation and strong wind gusts to 15 provinces (Figs. 5a and 5b). A maximum wind gust (sustained wind) of 41 m s−1 (34 m s−1) was recorded at Shengsi Island (Dachen Island). Winnie (1997) caused damage to 11 provinces and province-level municipalities from southeastern to northeastern China including Fujian, Zhejiang, Anhui, Shanghai, Jiangsu, Henan, Hebei, Shandong, Liaoning, Tianjin, and Jilin. The DELs of 10 provinces (except for Tianjin municipality, which does not have damage records) are shown in Fig. 6c. The most severe damage occurred in Zhejiang and Shandong Provinces, with DELs of 18.6 billion and 135 billion Yuan, respectively. Winnie (1997) resulted in the largest affected crop area (7159 thousand hectares) and the second highest DEL in the gross domestic production in China in the year of the TC (0.5859%), with a DEL of 43.63 billion based on data from 1984–2016.
Figure 5. The distributions of the total precipitation (units: mm) (a), maximum sustained wind (units: m s−1) (b), and direct economic losses (c) caused by TC Winnie (1997). The unit of the direct economic loss was 0.1 billion Chinese Yuan in 1997.
Figure 6. The distributions of the total precipitation (units: mm) (a), maximum sustained wind (units: m s−1) (b), and direct economic losses (c) caused by TC Bilis (2006). The unit of the direct economic loss was 0.1 billion Chinese Yuan in 2006.
The TC with the maximum IPT value was Bilis (2006). It first made landfall on Taiwan Province with a maximum wind speed of 30 m s−1 and made a second landfall on Xiapu, Fujian Province with the same intensity as the first landfall. Although the landfall intensity of Bilis (2006) was moderate, its long lifetime and trajectory over land brought long-term heavy precipitation over a large area (Fig. 6). Maximum total precipitation of 570 mm was recorded as lasting four days at Boluo station, Guangdong Province. Bilis (2006) caused more than 8000 (3000) landslides and mudslides in Bangzhou (Zixing) city, Hunan Province (Deng and Wang, 2007). It also brought strong wind gusts to both the coastal and inland areas (Fig. 6b). Sustained winds ranging from 10.8 m s−1 to 24.8 m s−1 were observed over Guangdong Province, the Qinzhou region in Guangxi Province, the local region in Yunnan Province, eastern Hubei Province and Hunan Province, the Jiujiang and Guangfeng regions in Jiangxi Province, the Pingba and Liuzhi regions in Guizhou Province, and most of Shanghai Province and Jiangsu Province. Wind gusts ranging from 32.7 m s−1 to 42.0 m s−1 were recorded over Yuhuan and Dachen Island in Zhejiang Province. The maximum wind gust was 42.0 m s−1 over Dachen Island. A total of 1057 deaths and damages of 38.31 billion Chinese Yuan were recorded over six provinces in 2006 including Zhejiang, Jiangxi, Hunan, Guangxi, Guangdong, and Fujian Provinces. The DEL of each province is shown in Fig. 6c. The top two provinces ranked by DEL were Guangdong and Hunan Provinces, with DEL values of 13.4 and 12.9 billion Chinese Yuan, respectively in 2006.
The percentages of each IPWT category from 0 to 4 were 7.41%, 37.86%, 25.51%, 16.46%, and 12.76%, respectively (Table 5). Notably, the CAT_IPWT values of all TCs shown in Table 4 belong to the super severe impact category (i.e., CAT_IPWT 4).
CAT_IPWT 0 1 2 3 4 Cases 36 184 124 80 62 Percentage (%) 7.41 37.86 25.51 16.46 12.76 Table 5. Frequency distribution of CAT_IPWT from 1960 to 2018.
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The maximum, mean, median, and minimum values of the annual total IPT, IWT, and IPWT values from 1960 to 2018 are listed in Table 6. The ranges of the annual total IPT, IWT, and IPWT values are 0.4485–3.5387, 0.1832–2.8673, and 0.3245–3.3987, respectively. The mean (median) of annual total IPT, IWT, and IPWT values were 1.9113 (1.8561), 1.1646 (1.0745), and 1.7367 (1.5615), respectively. 1998 was a unique year in that it had the lowest annual total IPT, IWT, and IPWT values.
Index IPT IWT IPWT Maximum 3.5387 (1994) 2.8673 (2005) 3.3987 (2018) Mean 1.9113 1.1646 1.7367 Median 1.8561 1.0745 1.5615 Minimum 0.4485 (1998) 0.1832 (1998) 0.3245 (1998) Table 6. Statistics of annual total IPT, IWT, and IPWT from 1960 to 2018. Number in the bracket is the corresponding year when it has maximum value or minimum value.
The time series of the annual total IPT, IWT, and IPWT values from 1960 to 2018 are shown in Fig. 7b. In agreement with previous studies (Ying et al., 2011a, b), a slight upward IPT trend and a downward IWT trend were found over the study period. If we focus only on the period from 1960 to 2004, slight downward trends are observed for both IWT and IPT; this result is similar to the trends reported in the results of Ren et al., (2006). A slight upward IPWT trend was also found from 1960 to 2018, although this trend was not statistically significant at the 95% confidence level according to the Mann-Kendall test. This means that the combined impact of TC-induced precipitation and wind over the Chinese mainland was increasing due to the increase in TC-induced precipitation.
Figure 7. Time series of the frequency of landfalling TCs and the IWT, IPT, and IPWT values from 1960 to 2018 with linear trends. (a) Annual frequency of TCs landfalling; (b) annual total; (c) annual mean; and (d) annual maximum.
Due to the decreasing trend of TCs landfalling over the Chinese mainland (Fig. 7a), the annual mean IPT and IPWT values showed more significant increasing trends than the trend of the annual total value (Fig. 7c), although the IWT series remained stable. The linear trend of the annual mean IPT (IPWT) was over the 90% (95%) confidence level. This means that the potential destructiveness of a single TC was increasing; this result aligned with the positive trends observed in the frequency and intensity of TC precipitation (Ying et al., 2011b; Zhang et al., 2013; Liu et al. 2020a ). The annual maximum IPT, IWT, and IPWT values are shown in Fig. 7d, and there are no significant linear trends.
The mean, maximum, median, and minimum values of the annual frequency of the IPWT categories are shown in Table 7. There were 1.36 Category 3 TCs and 1.05 Category 4 TCs per year. Both 2005 and 1985 were unique in having 4 TCs with Category 4 IPWT values and 5 TCs with Category 3 IPWT, which was the maximum frequency calculated in a year. The annual frequency of each category showed a differing trend from 1960 to 2018. The frequencies of Category 0 and Category 1 (Category 2 and Category 3) TCs showed downward (stable) trends (Fig. 8a) with statistical significance over (below) the 95% confidence level. However, the annual frequency and the percentage of landfalling Category 4 TCs showed increasing trends (Figs. 8a, c). Due to the decreasing annual frequency of landfalling TCs, the percentages of TCs of Categories 0–1 (Category 2) out of all storm categories still had downward (stable) trends (Fig. 8b). However, the proportion of Category 3 storms among the landfalling TCs started increasing slightly from a stable trend, and the percentage of Category 4 storms among the landfalling TCs showed a clearer increasing trend than did frequency (Fig. 8c). This means that the impacts of landfalling TCs, especially those resulting from the influences of heavy-impact TCs were increasing.
CAT_0 CAT_1 CAT_2 CAT_3 CAT_4 Mean 0.61 3.12 2.10 1.36 1.05 Maximum 2 7 (1967) 5 (1973,2008) 5 (1985) 4 (2005) Median 0 3 2 1 1 Minimum 0 0 0 0 0 Table 7. Statistics of the annual frequency of CAT_IPWT from 1960 to 2018. The numbers in parentheses show the year of the maximum annual frequency of CAT_IPWT.