Comprehensive Assessment and Variation Characteristics of the Drought Intensity in North China Based on EID

Haiyan Zhao aShanxi Climate Center, Taiyuan, Shanxi, China

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XianYan Chen bNational Climate Center, Beijing, China

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JiaXi Yang cInstitute of Urban Meteorology, China Meteorological Administration, Beijing, China

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Chuang Yao aShanxi Climate Center, Taiyuan, Shanxi, China

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Qiang Zhang bNational Climate Center, Beijing, China

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Ping Mei dNanjing University of Science and Technology, Nanjing, China

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Abstract

Under the new background of climate change, it is very important to identify the characteristics of drought in North China. Based on the daily meteorological drought comprehensive index from 494 national meteorological stations in North China during 1961–2019, the drought processes and their intensity are identified by applying the “extreme” intensity–duration (EID) theory. Then, the stage variation characteristics of the drought trend, the average drought intensity, and the drought frequency are analyzed. The results show that among the five drought intensity indices the process maximum intensity demonstrates the greatest correlation coefficient with the disaster rate of drought in North China. Therefore, the process maximum intensity of drought is selected as the annual drought intensity to analyze the drought characteristics in North China. According to the climate warming trends, the study period is divided into three stages, that is, 1951–84 (stage I), 1985–97 (stage II), and 1998–2019 (stage III). The comprehensive results show that the drought intensity in North China has significant stage characteristics. In stage I, the drought shows an increasing trend in most parts of North China, but its average intensity is relatively weaker, with a lower severe drought frequency. The drought also shows an increasing trend in most parts in stage II, with a more significant increase rate than that in stage I, and the average drought intensity is the strongest and the severe drought frequency is the highest. In stage III, the drought shows a decreasing trend in some areas, and the average intensity is the weakest, with a lower severe drought frequency.

Significance Statement

In this paper, we develop a drought intensity formula, the maximum intensity of drought, based on the “extreme” intensity–duration theory. The maximum intensity of drought was then calculated and selected as an annual drought intensity to analyze the drought characteristics in North China. We found that the annual drought intensity better captured the extremity and the patterns of drought process than that obtained with single indices and comprehensive indices. The results show a decreasing trend of drought in North China after 1998.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: XianYan Chen, chenxy@cma.gov.cn

Abstract

Under the new background of climate change, it is very important to identify the characteristics of drought in North China. Based on the daily meteorological drought comprehensive index from 494 national meteorological stations in North China during 1961–2019, the drought processes and their intensity are identified by applying the “extreme” intensity–duration (EID) theory. Then, the stage variation characteristics of the drought trend, the average drought intensity, and the drought frequency are analyzed. The results show that among the five drought intensity indices the process maximum intensity demonstrates the greatest correlation coefficient with the disaster rate of drought in North China. Therefore, the process maximum intensity of drought is selected as the annual drought intensity to analyze the drought characteristics in North China. According to the climate warming trends, the study period is divided into three stages, that is, 1951–84 (stage I), 1985–97 (stage II), and 1998–2019 (stage III). The comprehensive results show that the drought intensity in North China has significant stage characteristics. In stage I, the drought shows an increasing trend in most parts of North China, but its average intensity is relatively weaker, with a lower severe drought frequency. The drought also shows an increasing trend in most parts in stage II, with a more significant increase rate than that in stage I, and the average drought intensity is the strongest and the severe drought frequency is the highest. In stage III, the drought shows a decreasing trend in some areas, and the average intensity is the weakest, with a lower severe drought frequency.

Significance Statement

In this paper, we develop a drought intensity formula, the maximum intensity of drought, based on the “extreme” intensity–duration theory. The maximum intensity of drought was then calculated and selected as an annual drought intensity to analyze the drought characteristics in North China. We found that the annual drought intensity better captured the extremity and the patterns of drought process than that obtained with single indices and comprehensive indices. The results show a decreasing trend of drought in North China after 1998.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: XianYan Chen, chenxy@cma.gov.cn

1. Introduction

North China has the highest drought frequency in China (Zhou et al. 2014) and is one of the most severe drought areas in China (An et al. 2014). According to the statistics of agricultural drought disaster from the National Bureau of Statistics, the annual mean area affected by drought during 1961–2018 in North China is 6 388 000 ha, and the drought disaster area is 2 831 000 ha, which is much larger than that in other regions of China (http://www.stats.gov.cn/). The research on drought assessment methods in North China has always been a research hot spot. An accurate assessment of drought intensity can support the government in preventing and mitigating drought disasters.

To accurately monitor and assess the occurrence, development and change of drought, researchers have defined a series of drought indices based on meteorological and hydrological factors. Among them, the Palmer index (Cai et al. 2014; Zhai et al. 2010; Liu et al. 2004; Wang et al. 2016; Zhao et al. 2017), the standardized precipitation index (SPI) (Wang et al. 2014; Huang et al. 2015; Yang et al. 2019), the standardized evapotranspiration index (Li et al. 2012; Zhang et al. 2015; Beguería et al. 2014; Zhou and Wang 2020), the moist index (Ma and Ren 2007; Yao et al. 2015), and the comprehensive meteorological drought index (CI) (Zhang et al. 2006; Zou and Zhang 2008; Wang et al. 2007; Li et al. 2019; Guo et al. 2020; Yu and Zhai 2020) have been widely used in China. Subsequently, the CI has been improved by many scholars to reduce the irrational jump and increase the sensitivity to severe drought (Wang et al. 2011; Zhao et al. 2011a; Li et al. 2016). Thereby, a revised meteorological drought comprehensive index (MCI) has been developed (China National Standardization Administration 2017), which effectively improved the drought monitoring (Liao and Zhang 2017).

Although different drought indices were selected by researchers in monitoring and evaluating droughts, the assessment methods of drought intensity can be summarized as the analysis of average intensity, cumulative intensity, extreme intensity, and duration in a certain period. Specifically, Zhou et al. (2014) took the monthly mean standardized evapotranspiration index during the drought process as the drought intensity index in North China. Hu et al. (2017) analyzed the drought trend in North China based on the ratio of the annual precipitation to the annual evapotranspiration, indicating that the drought has an overall increasing trend in North China up to the 2010s (Zhou et al. 2014; Hu et al. 2017). The aggravating droughts in North China over recent decades were represented by the increase in both occurrence frequency and magnitude during the past 50 years (Sang et al. 2018). Moreover, the projection results of global climate models showed that the probability of concurrent drought events highly tend to increase from 2020 to 2050 (Liu et al. 2015).

However, there are significant uncertainties in the intensity assessment of drought events by using a single intensity index. For example, in the identification of drought events in Southwest China from 2009 to 2010, although both the cumulative intensity and the extreme intensity illustrated the four stages of the drought intensity variation, the cumulative intensity changed significantly while the change in extreme intensity was relatively flat and hysteretic. The drought assessments in Iran based on the extreme intensity, the longest duration and the average intensity were also greatly different when using the SPI (Saravi et al. 2009).

Therefore, to overcome the difficulty of a single index in determining the drought intensity, the methods of composite drought intensity and joint distribution function of drought duration–intensity have been developed (An et al. 2014; Ren et al. 2012; Li et al. 2015). An et al. (2014) used the objective identification method of extreme regional events (OITREE) to obtain 100 regional meteorological drought events in North China and classified them into 10 extreme events, 20 severe events, 40 moderate events, and 30 light events. Then, the weighted summation of the standardized extreme intensity, cumulative intensity, and duration were derived to thoroughly demonstrate the spatiotemporal variation characteristics of the drought events (Ren et al. 2012; Li et al. 2015). Sheffield et al. (2009) integrated the intensity, area, and duration of drought to identify extreme drought events with different durations and scales in the world. The comprehensive index can reduce the irrationality of a single index to a certain extent. However, the strong correlation between the single indices complicates the determination of the weight coefficients (Shiau 2006; Chen et al. 2016). Since Shiau (2006) established the joint distribution of drought duration–intensity with the copula method, domestic and foreign scholars have adopted the copula method to evaluate the drought events in the Yellow River basin, the upper reaches of the Hanjiang River, Chongqing City, and the United States (Shiau 2006; Yan et al. 2007; Lu et al. 2010; Chen et al. 2016; Xu et al. 2020) and calculated the return period of the joint distribution of drought duration–intensity. The copula method can better represent the occurrence and development of drought and its characteristics, but the choice of different copula models may lead to different results (Chen et al. 2016).

For the extreme precipitation, Lu et al. (2015) developed an “extreme” intensity–duration (EID) model to identify the maximum intensity in a precipitation process as its extreme intensity. Using the EID model to calculate the extreme intensity can avoid the disadvantage of unobvious extreme features caused by the fixed statistical period. Moreover, for a same precipitation process, the extreme intensity value calculated with the EID model is the largest. Therefore, the EID model can well characterize the extreme features of precipitation processes.

To prevent the uncertainty of single indices and comprehensive indices, we refer to the EID model proposed by Lu et al. (2015) and develop a drought equivalent intensity formula based on both the duration and the average intensity of a drought event. Furthermore, the maximum equivalent intensity in the process is obtained with a sliding, which is defined as the intensity of the drought process. The drought process intensity obtained with the EID model can better capture the extremity and the “catastrophability” of drought process than that obtained with single indices and comprehensive indices. The MCI is applied in this study, which has been widely used in meteorological operations. Based on the EID model, we analyze the stage variation characteristics of the trend, the intensity and the occurrence frequency of drought in North China after determining the drought processes and their intensity. This study aims to provide a scientific support for the government to formulate drought control and drought resistance policies. The rest of the paper is organized as follows. The data and methods used in this study are introduced in section 2. Section 3 shows the analysis results, and section 4 demonstrates the conclusions and discussion.

2. Data and methods

a. Data

The daily MCI is calculated based on the 1961–2019 daily temperature and precipitation data from 494 national meteorological stations in North China, including Shanxi Province, Hebei Province, Shandong Province, and Henan Province (Fig. 1). All the meteorological data are from the National Meteorological Information Center (http://data.cma.cn/) and processed for quality control. The drought-affected rate is calculated based on the drought-affected area and the total sowing area in four provinces of North China during 1961–2018. Reasonable drought indices should be consistent with the actual disaster. The drought disaster rate represents the actual disaster occurred by drought, so it is used to pick up drought index. The crop area affected by drought in different provinces is from the National Bureau of Statistics (http://www.stats.gov.cn/), and the sowing area data are from both the National Bureau of Statistics and the China planting information network (http://zzys.agri.gov.cn/).

Fig. 1.
Fig. 1.

Spatial distribution of 494 stations used in the study.

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0119.1

b. Determination of drought processes

When light or above drought lasts for not less than 15 consecutive days at a station, and the drought intensity reaches moderate or above grade at least one day, a drought process is determined. The first date with light drought during the drought process is defined as the drought onset. The drought process ends after 10 consecutive days with no drought or wetness. The last day with light or above drought during the drought process is the end date. The total days from the onset date to the end date (including the end date) are defined as the drought process days.

c. Drought process intensity

The intensity of a drought process is an important index of the process. Based on the EID method proposed by Lu et al. (2015), the intensity of a drought event (Zm) is defined as the maximum value of the equivalent cumulative intensity of the droughts with different durations during a drought process. The calculation method is
Zmn=maxk=1,m;n=1,k[S(m,n)],
where m is the total days of a drought process, and maxk=1,m;n=1,k[S(m, n)] denotes the maximum equivalent cumulative drought intensity determined with a sliding in period m when k = 1, 2, …, m (1 ≤ km) and n = 1, 2, …, k; S(m, n) is the equivalent cumulative drought intensity in n days calculated with Eq. (2), below.
The equivalent cumulative drought intensity is a comprehensive index reflecting the average drought intensity and the drought duration during a certain period. The larger its value is, the stronger the drought intensity is:
S(m,n)=naD(n)¯=na1nD(n)=na1i=1nIi,
where D(n)¯ denotes the average drought intensity and n is the drought duration (days). Here, a is the weight coefficient of time index with a range of 0.5–1.0, which is set to 0.5 in this work according to Lu et al. (2015). If the weight coefficient a = 1 then S(m, n) is the cumulative drought intensity, whereas if a = 0 then it is the average drought intensity. Also, Ii is the absolute value of the drought index on day i (1 ≤ in), and the drought index is 0 when the drought grade is below light.

In this study, Ii is calculated according to the national standard MCI, a comprehensive drought index, which takes into account the effective precipitation within the last 60 days (weight cumulative precipitation), 30-days evapotranspiration (relative humidity), seasonal-scale (90 days) precipitation, and near-semiannual-scale (150 days) precipitation. The calculation formula of MCI is shown in the national standard of “grades of meteorological drought” (GB/T 20481-2017) (China National Standardization Administration 2017). The drought grades corresponding to the MCI are shown in Table 1.

Table 1

Classification of drought grades according to the MCI.

Table 1

d. Annual drought intensity

The annual drought intensity Zs is defined as the greatest drought process intensity index in a certain year:
Zs= maxijZmni,
where j is the annual number of drought processes and Zmni is the intensity of a drought process in this year. If there is no drought process in this year, the annual drought intensity ZS = 0.

e. Regional annual drought intensity

The regional drought intensity Zd is defined as the average value of the drought intensity indices at all the single stations in the region:
Zd=1hihZsi,
where h denotes the total stations in the region and Zsi is the drought intensity at station i.

f. Trend analysis and grade classification

In this study, the least squares method is used to estimate the regression equation of the drought index and the drought disaster rate and that of the drought index and the year. The variation trend of the drought index is expressed by the regression coefficient of the drought index and the year (Wei 1999).

The standard deviation describes the evaluation distance between each data and the average value. The plus/minus standard deviation can give an interval to check whether there are abnormal values. Thus, (Zs¯+δZs) and (Zs¯δZs) are applied to express higher and lower than the average (Zhao et al. 2011b). Based on the average annual drought intensity Zs¯ and the standard deviation δZs from the mean drought intensity of 494 stations in North China during 1961–2019, the annual average drought intensity is classified into four grades, that is, the highest drought intensity, higher drought intensity, lower drought intensity, and the lowest drought intensity, as shown in Table 2.

Table 2

Grades of classification of annual drought intensity.

Table 2

The frequency of severe drought in this study refers to the frequency of the drought process with Zs ≥ 7.0. This threshold is selected based on the 50% percentile value of all the drought process intensities at all the stations in China, which makes the results in this study easier to compare with those in other regions. According to the average value and the standard deviation of the severe drought frequency at 494 stations in North China, the drought frequency can be classified into four grades, that is, the highest frequency, higher frequency, lower frequency, and the lowest frequency. The classification method is similar to that for the annual drought intensity.

3 Result analysis

a. Annual drought intensity and drought disasters

The maximum intensity of drought processes during 1961–2019 shows that the top three drought years in North China are 1968, 2001, and 2000, with the annual drought intensity of 152.3, 143.7, and 137.4, respectively. For the drought years based on the three indices of process peak, process average intensity and process days, the top three drought years based on the first two indices are basically consistent with those based on the maximum process intensity. The top three drought years based on the process days significantly differ from those based on the maximum process intensity, but 9 of the top 10 years are generally consistent with those based on the maximum process intensity. The top 10 drought years based on the indices of annual cumulative intensity and days of moderate drought and above are generally consistent with those based on the maximum process intensity, but the rankings are slightly different (Table 3).

Table 3

Top 10 drought years (ranked from 1 to 10, with 1 being the worst drought) in North China as based on six drought indices.

Table 3

The correlations between different drought intensity indices and the drought disaster rate show that all the drought intensity indices are significantly correlated with the drought disaster rate from 1961 to 2018 (P < 0.01). The correlation coefficients between the maximum process intensity and the drought disaster rate are the largest in Shanxi and Hebei, and the second largest correlation coefficients are in Shandong and Henan (Table 4). The correlations between all the drought indices and the drought disaster rate in Henan Province are obviously smaller than those in other provinces, which may be related to the stronger capability of drought control and drought resistance in Henan Province. Overall, the correlation coefficient between the maximum process intensity and the drought disaster rate in North China is the largest (Table 4), indicating that the extreme characteristics of the drought process can best illustrate the actual situation of the drought disaster. Therefore, this study selects the maximum process intensity as the annual drought intensity to analyze the drought characteristics in North China.

Table 4

Correlation coefficients between the drought indices and the drought disaster rate.

Table 4

b. Stage variation of drought

Xu et al. (2017) showed that the regional climate warming trend in China mutated in 1985 and 1998, with a cold period before 1984, a rapid warming period during 1985–97 and a slow warming period after 1998. Therefore, we divide the whole study period into three stages, that is, 1961–84 (stage I), 1985–97 (stage II), and 1998–2019 (stage III). Generally, the annual average drought intensity in North China shows a slight increasing trend in stage I, with the linear tendency rate of 0.3·per decade. In stage II, the increasing trend of drought intensity is more obvious, with a linear tendency rate up to 2.9·per decade. The drought trend takes a turning from increasing to decreasing in stage III, with a linear tendency rate of −1.1·per decade. The variation trend of drought intensity in North China is not significant in all the three stages. However, the increasing rate in stage II is obviously higher than that in stage I, and the trend turns from increasing to decreasing in stage III (Fig. 2). The variation of the annual average drought intensity shows prominent stage characteristics in North China. Therefore, we analyze the spatiotemporal characteristics of the drought in North China from three aspects of drought trend, average intensity and severe drought frequency in three stages.

Fig. 2.
Fig. 2.

The average drought intensity trends in North China from 1961 to 2019.

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0119.1

1) Changes in drought trends

During 1961–2019, the areas with reduced drought (61% of the stations) are more widespread than those with aggravated drought (39% of the stations) in North China (Fig. 3a). The areas with reduced drought are concentrated in north-central Shanxi Province, most of Hebei Province and western Shandong Province, with the reducing rate of ≥0.5·per decade at some stations in northern Shanxi Province and southern Hebei Province. The areas with aggravated drought are mainly distributed in southern Shanxi Province, most of Henan Province, eastern Shandong Province and northern Hebei Province. It is noted that only 5% of the stations demonstrate significant reducing or aggravating trends of drought intensity.

Fig. 3.
Fig. 3.

The variation in trends of drought in North China in (a) 1961–2019, (b) stage I, (c) stage II, and (d) stage III.

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0119.1

The analyses in different stages show that there are more stations (64%) with aggravated drought than those with reduced drought (36%) in stage I (Fig. 3b), and the aggravating trends are significant at 2% of the stations. In stage II, the drought aggravates in most of North China (80% of the stations) (Fig. 3c). The drought process intensity increases at a rate of ≥0.5·per decade in most of Shanxi Province, Hebei Province and Henan Province and northern Shandong Province, with the trends significant at 11% of the stations. Reducing trends drought intensity are found in eastern and southern Shandong Province, which are not significant. In stage III, the drought intensity reduces in most of North China (83% of the stations) (Fig. 3d), with the trends significant at 8% of the stations. Overall, the drought process intensity decreases at a rate below −0.5·per decade in most areas, and aggravating trends exist at 17% of the stations, which are all insignificant (Table 5).

Table 5

The number of stations with different drought trends during all of the stages in North China.

Table 5

The spatial distribution of the drought trends in the three stages shows relatively consistent stage variation characteristics at most stations in North China. Fenyang (Shanxi Province), Xinji (Hebei Province), Fangcheng (Henan Province), and Guangrao (Shandong Province) have complete and continuous meteorological observation data. The changes of temperature and precipitation at the four stations are consistent with average values of corresponding elements in the four provinces. Thus, typical stations are selected to analyze the stage variation characteristics of drought in North China. In stage I, the annual drought intensity at four stations increases or decreases insignificantly, with a tendency rate around ±1.0·per decade. In stage II, the annual drought intensity increases significantly at Fenyang station with the tendency rate being 6.1·per decade. In other three stations, the maximum annual drought intensity reaches 6.6·per decade (Xinji County, Hebei Province), but the variation trends are all insignificant. In stage III, the annual drought intensity exhibits insignificant decreasing trend from −2.3 to −1.0·per decade (Fig. 4). The analyses at these typical stations show that the variation trends of drought in North China are slight in stage I and the strongest in stage II. In stage III, the drought demonstrates weakening trends of different degrees.

Fig. 4.
Fig. 4.

The drought intensity trends at typical stations from 1961 to 2019.

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0119.1

2) Average intensity variation

The annual drought intensity has been classified into four grades, namely, the highest drought intensity, higher drought intensity, lower drought intensity, and the lowest drought intensity (Table 6). The distribution of the average drought intensity in North China during 1961–2019 shows that the higher drought intensity is mainly distributed in southern Hebei Province, south-central Shanxi Province, and northern Henan Province. The lower drought intensity is mainly distributed in northern Shanxi Province, northern Hebei Province, south-central Henan Province, and most of Shandong Province. In addition, the lowest drought intensity is found in some scattered areas, and there is no station with the highest drought intensity (Fig. 5a).

Fig. 5.
Fig. 5.

Patterns of average drought intensity in North China in (a) 1961–2019, (b) stage I, (c) stage II, and (d) stage III.

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0119.1

Table 6

Number of stations with different grades of average drought intensity in each stage in North China.

Table 6

The distributions in different stages show that the highest drought intensity is mainly distributed in south-central Hebei Province in stage I, and the drought intensity in other grades is scattered. In stage II, the highest and higher drought intensity areas are expanded, which are mainly distributed in south-central Shanxi Province, most of Shandong Province, and northern Henan Province. In stage III, the area of the highest drought intensity is reduced. However, the lowest and lower drought intensity areas are widely expanded, which occupy most stations except northern Henan Province (Figs. 5b–d).

The statistics in Table 6 show that the lower drought intensity occupies the most stations among all the intensity grades (179 stations) in stage I. In addition, the number of stations with the highest and higher drought intensity is very close to that with the lowest and lower drought intensity. In stage II, the higher drought intensity occupies the most stations among all the intensity grades (193 stations), and the stations with high drought intensity are more than those with low drought intensity. In stage III, lower drought intensity occupies not only the most stations among all the drought grades in this stage, but also the most stations among the three stages, reaching 304 stations. Moreover, the stations with higher drought intensity are far less than those with lower drought intensity. Therefore, the drought intensity is the strongest in stage II and the weakest in stage III.

3) Variation of severe drought frequency

The frequency distributions of different grades of severe drought in North China are analyzed based on the classification method in section 2f (Table 7). The results show that there are few stations with the highest frequency of severe drought in North China from 1961 to 2019, which are scattered in central Shanxi Province and south-central Hebei Province. The stations with higher frequency are mainly distributed in south-central Hebei Province, south-central Shanxi Province, and northern Henan Province, and the stations with lower frequency are widely distributed in most areas except southern Hebei Province. In addition, the stations with the lowest frequency are scattered in northern Shanxi Province, southern Henan Province, and other areas (Fig. 6a).

Fig. 6.
Fig. 6.

The frequency patterns of severe drought in North China in (a) 1961–2019, (b) stage I, (c) stage II, and (d) stage III.

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0119.1

Table 7

Number of stations with different grades of severe drought frequency in each stage in North China.

Table 7

The analyses in the three stages show that, in stage I, the stations with the highest frequency of severe drought are mainly distributed in south-central Hebei Province, and the stations with other grades of frequency are relatively dispersed. The areas of the highest frequency of severe drought are expanded in stage II, mainly distributed in central Shanxi Province and most of Shandong Province. In stage III, the areas of the highest frequency are reduced, while the areas of the lowest frequency and lower frequency are widely expanded, which are distributed in most regions except southern Shanxi Province and northern Henan Province (Figs. 6b–d).

The statistics in Table 7 show that, in stage I, the stations with lower frequency of severe drought are the most (189 stations). Similarly, the lower frequency of severe drought also occupies the most stations in stage II (208 stations). In addition, the number of stations with the highest frequency reaches 120, which is the largest among the three stages. In stage III, the number of stations with lower frequency is also the largest, while the number is much greater that those in the other two stages, reaching 283. On the other hand, the number of stations with the highest frequency is only 28 in stage III, which is much smaller than that in the other two stages. Therefore, the stations with the highest frequency of severe drought are the most in stage II and the least in stage III, but the stations with lower frequency are the most in stage III.

Figures 5 and 6 show the spatiotemporal distributions of drought intensity and frequency at different levels. It can be found that in stage I, the distributions of the highest and higher drought intensity and frequency are relatively concentrated. In stage II, although the number of stations with the highest drought intensity and frequency increases slightly, their distributions have changed significantly. Moreover, the highest and higher drought intensity and frequency are moving from the center of North China (southern Hebei Province) to the southern (southern Shanxi Province and northern Henan Province) and eastern parts (Shandong Province) of North China, and drought is relieved in most areas of North China in stage III. The main reason is that the response to climate warming is different among different sites.

In the center of North China, precipitation at many sites is increasing. For example, the precipitation at Jinzhou next to Xinji (Fig. 1) increases from 455.0 mm in stage I to 480.7 mm in stage II. So, drought intensity and frequency at Jinzhou decrease in stage II. But, precipitation at some sites in the northern part of Hebei Province decreases. For example, precipitation at Xinji (Fig. 1) decreases from 507.3 mm in stage I to 469.2 mm in stage II, so drought intensity and frequency both have aggravating trends in stage II. As for the eastern part of North China (Shandong Province), precipitation generally decreases, for example, precipitation in Guangrao decreases obviously from 610.3 mm in stage I to 543.0 mm in stage II, and drought intensity and frequency in Guangrao increase in stage II.

4. Conclusions and discussion

a. Conclusions

The top 10 drought years based on various drought intensity indices are generally consistent in North China from 1961 to 2018, but the rankings are slightly different. In North China, the correlation coefficient between the maximum process intensity and the drought disaster rate is the largest, indicating that the extreme characteristics of the drought process can best illustrate the actual situation of the drought disaster. Therefore, we select the maximum process intensity as the annual drought intensity to analyze the drought characteristics in North China.

The variation trends of the regional average intensity are not significant in the three stages in North China, but the drought aggravating rate is significantly higher in stage II than in stage I. In addition, the drought trend turns from aggravating to reducing in stage III. There are more stations with aggravating drought than those with reducing drought in stage I in North China. In stage II, there is a consistent aggravating trend of drought. On the contrary, most of the stations in North China exhibit a consistent reducing trend of drought in stage III.

In stages I and III, lower drought intensity occupies the most stations, while in stage II, higher drought intensity exists at the most stations. Moreover, the drought intensity is the strongest in stage II and the weakest in stage III.

In stage I, lower frequency of severe drought is found at the most stations. In stage II, the highest frequency of severe drought occupies the most stations. In stage III, the stations with lower frequency are far more than those in the other two stages, while the stations with the highest frequency is the least among the three stages. The highest frequency of severe drought occupies the most stations in stage II and the least stations in stage III. Moreover, the stations with lower frequency of severe drought are the most in stage III.

The comprehensive analysis results of the stage variation trend, the average intensity and the severe drought frequency show that the drought in North China is relatively lighter in stage I and the most serious in stage II, and then the drought weakens in stage III.

b. Discussion

North China was in a period with less rainfall in the 1980s, with an obvious aridity trend (Liao and Zhang 2017; Hao and Ding 2012; Zou and Zhang 2008; Fu and Ma 2008; Ju et al. 2006; Zhang 1999). This is consistent with the findings in stage II in this study, which is related to the weakening of the East Asian summer monsoon circulation caused by the positive anomalies of the sea surface temperature in the equatorial Pacific and the tropical Indian Ocean (Hao and Ding 2012; Ju et al. 2006).

Under the new climate background, the drought trend after 1998 experienced a tipping point in North China, which is consistent with the results demonstrating an increasing trend and then a declining trend of drought in recent 50 years based on the standardized precipitation evapotranspiration index and the Palmer drought severity index (Zhou et al. 2014; Ma et al. 2018). However, this conclusion is different from the results of Zhang et al. (2015), Zhang et al. (2016), and An et al. (2014), which is possibly related to the incomplete consistency of the study area. Specifically, the study region in the above studies does not include Shanxi Province, which is included in this study. The increasing trend of annual precipitation in Shanxi Province after 1998 is the most significant among the four provinces in North China (Hebei, Henan, Shanxi, and Shandong), which contributes greatly to the weakening trend of drought in this stage. In addition, the time length of the data series may also be a reason for the above inconsistency. The two studies of Zhang et al. (2015) and Zhang et al. (2016) were up to 2010 and 2012, respectively. However, the precipitation in China increased continuously from 2012 to 2018 (China Meteorological Administration 2019), resulting in a more obvious turning point of the arid trend in North China.

Although the variation trend and intensity of drought in North China have weakened since 1998, the drought disaster in North China is still more serious than that in southern China (Zhao et al. 2021). Therefore, under the new climate background, the drought control and drought resistance still need to be strengthened in North China.

Acknowledgments.

We thank Nanjing Hurricane Translation for reviewing the English language quality of this paper. This work was supported by National Key Research and Development Program of China (2017YFC1502402, 2018YFC1507700, 2017YFD0300201, and 2017YFA0605004) and Shanxi Scholarship Council of China (2020-164).

Data availability statement.

Because of confidentiality agreements, all the meteorological data can only be made available to bona fide researchers subject to a nondisclosure agreement. Details of the data and how to request access are available from datacenter@cma.gov.cn at the National Meteorological Information Center (http://data.cma.cn/). Drought disasters data from 1978 to 2019 are openly available from the National Bureau of Statistics (http://www.stats.gov.cn/), and these kinds of data during the years from 1961 to 1977 are from the China planting information network downloaded 10 years ago; details of the data and how to request access are available from 010-59193366 at the China planting information network (http://zzys.agri.gov.cn/).

REFERENCES

  • An, L. J., and Coauthors, 2014: Study on characteristics of regional drought events over North China during the past 50 years. Meteor. Mon., 40, 10971105.

    • Search Google Scholar
    • Export Citation
  • Beguería, S., S. M. Vicente‐Serrano, F. Reig, and B. Latorre, 2014: Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol., 34, 30013023, https://doi.org/10.1002/joc.3887.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, Q., Y. Liu, Y. Lei, G. Bao, and B. Sun, 2014: Reconstruction of the March-August PDSI since 1703 AD based on tree rings of Chinese pine (Pinus tabulaeformis Carr.) in the Lingkong Mountain, southeast Chinese loess Plateau. Climate Past, 10, 509521, https://doi.org/10.5194/cp-10-509-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Z. Q., W. Hou, D. Zuo, and J. Hu, 2016: Research on drought characteristics in China based on the revised copula function. J. Arid Meteor., 34, 213222.

    • Search Google Scholar
    • Export Citation
  • China Meteorological Administration, 2019: China Climate Bulletin 2018. CMA, 56 pp., http://www.cma.gov.cn/root7/auto13139/201903/t20190319_517664.html.

    • Search Google Scholar
    • Export Citation
  • China National Standardization Administration, 2017: Classification of meteorological drought. National Climate and Climate Change Standardization Technical Committee Rep. GB/T20481-2017, https://www.chinesestandard.net/PDF/English.aspx/GBT20481-2017.

    • Search Google Scholar
    • Export Citation
  • Fu, C. B., and Z. G. Ma, 2008: Global change and regional aridification. Chin. J. Atmos. Sci., 32, 752760, https://doi.org/10.3878/j.issn.1006-9895.2008.04.05.

    • Search Google Scholar
    • Export Citation
  • Guo, A. H., and Coauthors, 2020: Analysis of maize water deficit and drought intensity under different precipitation guarantee rates in northeastern China. Agri. Res. Arid Areas, 38, 266273.

    • Search Google Scholar
    • Export Citation
  • Hao, L. S., and Y. H. Ding, 2012: Progress of precipitation research in North China. Prog. Geogr., 31, 593601, https://doi.org/10.11820/dlkxjz.2012.05.007.

    • Search Google Scholar
    • Export Citation
  • Hu, Q., B. Dong, X. Pan, X. Wang, P. Wei, H. Zhao, and X. Zhang, 2017: Spatiotemporal variation and causes analysis of dry-wet climate at different time scales in North China Plain. Chin. J. Agrometeor., 38, 267277, https://doi.org/10.3969/j.issn.1000-6362.2017.05.001.

    • Search Google Scholar
    • Export Citation
  • Huang, J., Y. Xue, S. L. Sun, and J. Zhang, 2015: Spatial and temporal variability of drought during 1960–2012 in Inner Mongolia, north China. Quat. Int., 355, 134144, https://doi.org/10.1016/j.quaint.2014.10.036.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ju, J. H., J. M. Lv, and J. Z. Ren, 2006: The effect of interdecadal variations of Arctic Oscillation on aridization in North China. Plateau Meteor., 25, 7481.

    • Search Google Scholar
    • Export Citation
  • Li, N., Z.-G. Huo, J.-X. Qian, J.-J. Xiao, and X.-Y. Zhou, 2019: Spatiotemporal distribution of drought in Shanxi Province based on modified relative moisture index. Chin. J. Ecol., 38, 22492257, https://doi.org/10.13292/j.1000-4890.201907.037.

    • Search Google Scholar
    • Export Citation
  • Li, Q. L., G. Fan, D. Zhou, Z. Jiang, and J. Yu, 2016: On modification of meteorological drought composite index in Southwest China. J. Southwest China Norm. Univ., 41, 138146, https://doi.org/10.13718/j.cnki.xsxb.2016.01.024.

    • Search Google Scholar
    • Export Citation
  • Li, W. G., M. T. Hou, H. L. Chen, and X. Chen, 2012: Study on drought trend in south China based on standardized precipitation evapotranspiration index. J. Nat. Disasters, 21, 8490.

    • Search Google Scholar
    • Export Citation
  • Li, Y. P., J. S. Wang, and Y. H. Li, 2015: Characteristics of a regional meteorological drought event in southwestern China during 2009-2010. J. Arid Meteor., 4, 537545, https://doi.org/CNKI:SUN:GSQX.0.2015-04-001.

    • Search Google Scholar
    • Export Citation
  • Liao, Y. M., and C. J. Zhang, 2017: Spatial temporal distribution characteristics and disaster change of drought in China based on meteorological drought composite index. Meteor. Mon., 43, 14021409.

    • Search Google Scholar
    • Export Citation
  • Liu, W., S. An, G. Liu, and G. Anhong, 2004: The farther modification of Palmer drought severity model. J. Appl. Meteor. Sci., 15, 207216.

    • Search Google Scholar
    • Export Citation
  • Liu, X., Y. Luo, T. Yang, K. Liang, M. Zhang, and C. Liu, 2015: Investigation of the probability of concurrent drought events between the water source and destination regions of China’s water diversion project. Geophys. Res. Lett., 42, 84248431, https://doi.org/10.1002/2015GL065904.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, E., and Coauthors, 2015: Determining starting time and duration of extreme precipitation events based on intensity. Climate Res., 63, 3141, https://doi.org/10.3354/cr01280.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, G. H., G.-X. Yan, Z.-Y. Wu, and Y.-X. Kang, 2010: Regional drought analysis approach based on copula function. Adv. Water Sci., 21, 188193.

    • Search Google Scholar
    • Export Citation
  • Ma, Z. G., and X. B. Ren, 2007: Drying trend over China from 1951 to 2006. Adv. Climate Change Res., 3, 195201.

  • Ma, Z. G., C. Fu, Q. Yang, Z. Zheng, M. Lv, M. Li, Y. Duan, and L. Chen, 2018: Drying trend in northern China and its shift during 1951–2016. Chin. J. Atmos. Sci., 42, 951961.

    • Search Google Scholar
    • Export Citation
  • Ren, F., D. L. Cui, and Z. Q. Gong, 2012: An objective identification technique for regional extreme events. J. Climate, 25, 70157027, https://doi.org/10.1175/JCLI-D-11-00489.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sang, Y. F., V. P. Singh, and Z. Hu, 2018: Entropy-aided evaluation of meteorological droughts over China. J. Geophys. Res. Atmos., 123, 740749, https://doi.org/10.1002/2017JD026956.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saravi, M. M., A. A. Safdari, and A. Malekian, 2009: Intensity-duration-frequency and spatial analysis of droughts using the standardized precipitation index. Hydrol. Earth Syst. Sci. Discuss., 6, 13471383, https://doi.org/10.5194/hessd-6-1347-2009.

    • Search Google Scholar
    • Export Citation
  • Sheffield, J., K. M. Andreadis, E. F. Wood, and D. P. Lettenmaier, 2009: Global and continental drought in the second half of the twentieth century: Severity–area–duration analysis and temporal variability of large-scale events. J. Climate, 22, 19621981, https://doi.org/10.1175/2008JCLI2722.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shiau, J. T., 2006: Fitting drought and severity with two-dimensional copulas. Water Resour. Manage., 20, 795815, https://doi.org/10.1007/s11269-005-9008-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, C. L., J. Guo, L.-F. Xue, and L.-J. Ding, 2011: An improved comprehensive meteorological drought index CI_new and its applicability analysis. Chin. J. Agrometeor., 32, 621626, https://doi.org/10.3969/j.issn.1000-6362.2011.04.023.

    • Search Google Scholar
    • Export Citation
  • Wang, S. P., C. J. Zhang, Y. H. Li, J. Feng, and J. Wang, 2014: Analysis of multi-timescale drought variation based on standardized precipitation index in China during 1960–2011. J. Desert Res., 34, 827834.

    • Search Google Scholar
    • Export Citation
  • Wang, W., Z. Xu, X. Cai, and J. Gao, 2016: Aridity characteristic in middle and lower reaches of Yangtze River area based on Palmer drought severity index analysis. Plateau Meteor., 35, 693707, https://doi.org/10.7522/j.issn.1000-0534.2015.00011.

    • Search Google Scholar
    • Export Citation
  • Wang, Y. M., Q. Zhang, and X. K. Zou, 2007: Research progress of drought indicators and introduction of national drought monitoring services. Proc. Climatol. Soc. China, 8, 238245.

    • Search Google Scholar
    • Export Citation
  • Wei, F. Y., 1999: Statistical Diagnosis and Prediction Technology of Modern Climate. China Meteorological Press, 276 pp.

  • Xu, L., P. Abbaszadeh, H. Moradkhani, N. Chen, and X. Zhang, 2020: Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index. Remote Sens. Environ., 250, 112028, https://doi.org/10.1016/j.rse.2020.112028.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, Y., G. L. Tang, and Q. Zhang, 2017: Analysis of the variation of the air temperature over China during the global warming hiatus period. Acta Meteor. Sin., 13, 569577.

    • Search Google Scholar
    • Export Citation
  • Yan, B.-W., S.-L. Guo, Y. Xiao, and B. Fang, 2007: Analysis on drought characteristics based on bivariate joint distribution. Arid Zone Res., 24, 537542.

    • Search Google Scholar
    • Export Citation
  • Yang, G. Q., J. Z. Wang, and M. M. Sun, 2019: Spatial and temporal characteristics of drought in Cangzhou of Hebei Province based on standardized precipitation index. J. Arid Meteor., 37, 218225.

    • Search Google Scholar
    • Export Citation
  • Yao, Y. B., and Coauthors, 2015: Temporal-spatial abnormity of drought for climate warning in Southwest China. Resour. Sci., 37, 17741784.

    • Search Google Scholar
    • Export Citation
  • Yu, R., and P. M. Zhai, 2020: Changes in compound drought and hot extreme events in summer over populated eastern China. Wea. Climate Extremes, 30, 100295, https://doi.org/10.1016/j.wace.2020.100295.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhai, J., B. Su, V. Krysanova, T. Vetter, C. Gao, and T. Jiang, 2010: Spatial variation and trends in PDSI and SPI indices and their relation to streamflow in 10 large regions of China. J. Climate, 23, 649663, https://doi.org/10.1175/2009JCLI2968.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, H. L., Q. Zhang, Q. Liu, and P. Yan, 2016: Analysis on variation characteristics and differences of the climate drying degree between South and North of China. Plateau Meteor., 35, 13391351, https://doi.org/10.7522/j.issn.1000-0534.2015.00099.

    • Search Google Scholar
    • Export Citation
  • Zhang, Q., and Coauthors, 2006: GB/T 20481-2006, Grades of Meteorological Drought. China Standards Press, 22 pp.

  • Zhang, Q. Y., 1999: The variations of the precipitation and water resources in North China since 1880. Plateau Meteor., 18, 486495.

  • Zhang, Y. J., C. Y. Wang, and J. Q. Zhang, 2015: Analysis of the spatial and temporal characteristics of drought in the North China plain based on standardized precipitation evaporation index. Acta Ecol. Sin., 35, 70977107, https://doi.org/10.5846/stxb201311272825.

    • Search Google Scholar
    • Export Citation
  • Zhao, H. Y., G. Gao, P. Zhang, and X. Yan, 2011a: The modification of meteorological drought composite index and its application in Southwest China. J. Appl. Meteor. Sci., 22, 698705.

    • Search Google Scholar
    • Export Citation
  • Zhao, H. Y., G. Gao, X. Yan, Q. Zhang, M. Hou, Y. Zhu, and Z. Tian, 2011b: Risk assessment of agricultural drought using the CERES-Wheat model: A case study of Henan Plain, China. Climate Res., 50, 247256, https://doi.org/10.3354/cr01060.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H. Y., G. Gao, W. An, X. Zou, H. Li, and M. Hou, 2017: Timescale differences between SC-PDSI and SPEI for drought monitoring in China. Phys. Chem. Earth, 102, 4858, https://doi.org/10.1016/j.pce.2015.10.022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H. Y., and Coauthors, 2021: Temporal and spatial characteristics of drought in China under climate change. Chin. J. Agrometeor., 42, 6979.

    • Search Google Scholar
    • Export Citation
  • Zhou, D., and Coauthors, 2014: SPEI-based intensity characteristics and cause analysis of drought in north China during recent 50 years. J. Nat. Disasters, 23, 193202.

    • Search Google Scholar
    • Export Citation
  • Zhou, K., and Y. Wang, 2020: Temporal and spatial distribution of multi-scale drought in North China. IOP Conf. Ser.: Earth and Environ. Sci., 428, 012057, https://doi.org/10.1088/1755-1315/428/1/012057.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zou, X. K., and Q. Zhang, 2008: Preliminary studies on variations in droughts over China during past 50 years. J. Appl. Meteor. Sci., 19, 679687.

    • Search Google Scholar
    • Export Citation
Save
  • An, L. J., and Coauthors, 2014: Study on characteristics of regional drought events over North China during the past 50 years. Meteor. Mon., 40, 10971105.

    • Search Google Scholar
    • Export Citation
  • Beguería, S., S. M. Vicente‐Serrano, F. Reig, and B. Latorre, 2014: Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol., 34, 30013023, https://doi.org/10.1002/joc.3887.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, Q., Y. Liu, Y. Lei, G. Bao, and B. Sun, 2014: Reconstruction of the March-August PDSI since 1703 AD based on tree rings of Chinese pine (Pinus tabulaeformis Carr.) in the Lingkong Mountain, southeast Chinese loess Plateau. Climate Past, 10, 509521, https://doi.org/10.5194/cp-10-509-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Z. Q., W. Hou, D. Zuo, and J. Hu, 2016: Research on drought characteristics in China based on the revised copula function. J. Arid Meteor., 34, 213222.

    • Search Google Scholar
    • Export Citation
  • China Meteorological Administration, 2019: China Climate Bulletin 2018. CMA, 56 pp., http://www.cma.gov.cn/root7/auto13139/201903/t20190319_517664.html.

    • Search Google Scholar
    • Export Citation
  • China National Standardization Administration, 2017: Classification of meteorological drought. National Climate and Climate Change Standardization Technical Committee Rep. GB/T20481-2017, https://www.chinesestandard.net/PDF/English.aspx/GBT20481-2017.

    • Search Google Scholar
    • Export Citation
  • Fu, C. B., and Z. G. Ma, 2008: Global change and regional aridification. Chin. J. Atmos. Sci., 32, 752760, https://doi.org/10.3878/j.issn.1006-9895.2008.04.05.

    • Search Google Scholar
    • Export Citation
  • Guo, A. H., and Coauthors, 2020: Analysis of maize water deficit and drought intensity under different precipitation guarantee rates in northeastern China. Agri. Res. Arid Areas, 38, 266273.

    • Search Google Scholar
    • Export Citation
  • Hao, L. S., and Y. H. Ding, 2012: Progress of precipitation research in North China. Prog. Geogr., 31, 593601, https://doi.org/10.11820/dlkxjz.2012.05.007.

    • Search Google Scholar
    • Export Citation
  • Hu, Q., B. Dong, X. Pan, X. Wang, P. Wei, H. Zhao, and X. Zhang, 2017: Spatiotemporal variation and causes analysis of dry-wet climate at different time scales in North China Plain. Chin. J. Agrometeor., 38, 267277, https://doi.org/10.3969/j.issn.1000-6362.2017.05.001.

    • Search Google Scholar
    • Export Citation
  • Huang, J., Y. Xue, S. L. Sun, and J. Zhang, 2015: Spatial and temporal variability of drought during 1960–2012 in Inner Mongolia, north China. Quat. Int., 355, 134144, https://doi.org/10.1016/j.quaint.2014.10.036.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ju, J. H., J. M. Lv, and J. Z. Ren, 2006: The effect of interdecadal variations of Arctic Oscillation on aridization in North China. Plateau Meteor., 25, 7481.

    • Search Google Scholar
    • Export Citation
  • Li, N., Z.-G. Huo, J.-X. Qian, J.-J. Xiao, and X.-Y. Zhou, 2019: Spatiotemporal distribution of drought in Shanxi Province based on modified relative moisture index. Chin. J. Ecol., 38, 22492257, https://doi.org/10.13292/j.1000-4890.201907.037.

    • Search Google Scholar
    • Export Citation
  • Li, Q. L., G. Fan, D. Zhou, Z. Jiang, and J. Yu, 2016: On modification of meteorological drought composite index in Southwest China. J. Southwest China Norm. Univ., 41, 138146, https://doi.org/10.13718/j.cnki.xsxb.2016.01.024.

    • Search Google Scholar
    • Export Citation
  • Li, W. G., M. T. Hou, H. L. Chen, and X. Chen, 2012: Study on drought trend in south China based on standardized precipitation evapotranspiration index. J. Nat. Disasters, 21, 8490.

    • Search Google Scholar
    • Export Citation
  • Li, Y. P., J. S. Wang, and Y. H. Li, 2015: Characteristics of a regional meteorological drought event in southwestern China during 2009-2010. J. Arid Meteor., 4, 537545, https://doi.org/CNKI:SUN:GSQX.0.2015-04-001.

    • Search Google Scholar
    • Export Citation
  • Liao, Y. M., and C. J. Zhang, 2017: Spatial temporal distribution characteristics and disaster change of drought in China based on meteorological drought composite index. Meteor. Mon., 43, 14021409.

    • Search Google Scholar
    • Export Citation
  • Liu, W., S. An, G. Liu, and G. Anhong, 2004: The farther modification of Palmer drought severity model. J. Appl. Meteor. Sci., 15, 207216.

    • Search Google Scholar
    • Export Citation
  • Liu, X., Y. Luo, T. Yang, K. Liang, M. Zhang, and C. Liu, 2015: Investigation of the probability of concurrent drought events between the water source and destination regions of China’s water diversion project. Geophys. Res. Lett., 42, 84248431, https://doi.org/10.1002/2015GL065904.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, E., and Coauthors, 2015: Determining starting time and duration of extreme precipitation events based on intensity. Climate Res., 63, 3141, https://doi.org/10.3354/cr01280.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, G. H., G.-X. Yan, Z.-Y. Wu, and Y.-X. Kang, 2010: Regional drought analysis approach based on copula function. Adv. Water Sci., 21, 188193.

    • Search Google Scholar
    • Export Citation
  • Ma, Z. G., and X. B. Ren, 2007: Drying trend over China from 1951 to 2006. Adv. Climate Change Res., 3, 195201.

  • Ma, Z. G., C. Fu, Q. Yang, Z. Zheng, M. Lv, M. Li, Y. Duan, and L. Chen, 2018: Drying trend in northern China and its shift during 1951–2016. Chin. J. Atmos. Sci., 42, 951961.

    • Search Google Scholar
    • Export Citation
  • Ren, F., D. L. Cui, and Z. Q. Gong, 2012: An objective identification technique for regional extreme events. J. Climate, 25, 70157027, https://doi.org/10.1175/JCLI-D-11-00489.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sang, Y. F., V. P. Singh, and Z. Hu, 2018: Entropy-aided evaluation of meteorological droughts over China. J. Geophys. Res. Atmos., 123, 740749, https://doi.org/10.1002/2017JD026956.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saravi, M. M., A. A. Safdari, and A. Malekian, 2009: Intensity-duration-frequency and spatial analysis of droughts using the standardized precipitation index. Hydrol. Earth Syst. Sci. Discuss., 6, 13471383, https://doi.org/10.5194/hessd-6-1347-2009.

    • Search Google Scholar
    • Export Citation
  • Sheffield, J., K. M. Andreadis, E. F. Wood, and D. P. Lettenmaier, 2009: Global and continental drought in the second half of the twentieth century: Severity–area–duration analysis and temporal variability of large-scale events. J. Climate, 22, 19621981, https://doi.org/10.1175/2008JCLI2722.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shiau, J. T., 2006: Fitting drought and severity with two-dimensional copulas. Water Resour. Manage., 20, 795815, https://doi.org/10.1007/s11269-005-9008-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, C. L., J. Guo, L.-F. Xue, and L.-J. Ding, 2011: An improved comprehensive meteorological drought index CI_new and its applicability analysis. Chin. J. Agrometeor., 32, 621626, https://doi.org/10.3969/j.issn.1000-6362.2011.04.023.

    • Search Google Scholar
    • Export Citation
  • Wang, S. P., C. J. Zhang, Y. H. Li, J. Feng, and J. Wang, 2014: Analysis of multi-timescale drought variation based on standardized precipitation index in China during 1960–2011. J. Desert Res., 34, 827834.

    • Search Google Scholar
    • Export Citation
  • Wang, W., Z. Xu, X. Cai, and J. Gao, 2016: Aridity characteristic in middle and lower reaches of Yangtze River area based on Palmer drought severity index analysis. Plateau Meteor., 35, 693707, https://doi.org/10.7522/j.issn.1000-0534.2015.00011.

    • Search Google Scholar
    • Export Citation
  • Wang, Y. M., Q. Zhang, and X. K. Zou, 2007: Research progress of drought indicators and introduction of national drought monitoring services. Proc. Climatol. Soc. China, 8, 238245.

    • Search Google Scholar
    • Export Citation
  • Wei, F. Y., 1999: Statistical Diagnosis and Prediction Technology of Modern Climate. China Meteorological Press, 276 pp.

  • Xu, L., P. Abbaszadeh, H. Moradkhani, N. Chen, and X. Zhang, 2020: Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index. Remote Sens. Environ., 250, 112028, https://doi.org/10.1016/j.rse.2020.112028.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, Y., G. L. Tang, and Q. Zhang, 2017: Analysis of the variation of the air temperature over China during the global warming hiatus period. Acta Meteor. Sin., 13, 569577.

    • Search Google Scholar
    • Export Citation
  • Yan, B.-W., S.-L. Guo, Y. Xiao, and B. Fang, 2007: Analysis on drought characteristics based on bivariate joint distribution. Arid Zone Res., 24, 537542.

    • Search Google Scholar
    • Export Citation
  • Yang, G. Q., J. Z. Wang, and M. M. Sun, 2019: Spatial and temporal characteristics of drought in Cangzhou of Hebei Province based on standardized precipitation index. J. Arid Meteor., 37, 218225.

    • Search Google Scholar
    • Export Citation
  • Yao, Y. B., and Coauthors, 2015: Temporal-spatial abnormity of drought for climate warning in Southwest China. Resour. Sci., 37, 17741784.

    • Search Google Scholar
    • Export Citation
  • Yu, R., and P. M. Zhai, 2020: Changes in compound drought and hot extreme events in summer over populated eastern China. Wea. Climate Extremes, 30, 100295, https://doi.org/10.1016/j.wace.2020.100295.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhai, J., B. Su, V. Krysanova, T. Vetter, C. Gao, and T. Jiang, 2010: Spatial variation and trends in PDSI and SPI indices and their relation to streamflow in 10 large regions of China. J. Climate, 23, 649663, https://doi.org/10.1175/2009JCLI2968.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, H. L., Q. Zhang, Q. Liu, and P. Yan, 2016: Analysis on variation characteristics and differences of the climate drying degree between South and North of China. Plateau Meteor., 35, 13391351, https://doi.org/10.7522/j.issn.1000-0534.2015.00099.

    • Search Google Scholar
    • Export Citation
  • Zhang, Q., and Coauthors, 2006: GB/T 20481-2006, Grades of Meteorological Drought. China Standards Press, 22 pp.

  • Zhang, Q. Y., 1999: The variations of the precipitation and water resources in North China since 1880. Plateau Meteor., 18, 486495.

  • Zhang, Y. J., C. Y. Wang, and J. Q. Zhang, 2015: Analysis of the spatial and temporal characteristics of drought in the North China plain based on standardized precipitation evaporation index. Acta Ecol. Sin., 35, 70977107, https://doi.org/10.5846/stxb201311272825.

    • Search Google Scholar
    • Export Citation
  • Zhao, H. Y., G. Gao, P. Zhang, and X. Yan, 2011a: The modification of meteorological drought composite index and its application in Southwest China. J. Appl. Meteor. Sci., 22, 698705.

    • Search Google Scholar
    • Export Citation
  • Zhao, H. Y., G. Gao, X. Yan, Q. Zhang, M. Hou, Y. Zhu, and Z. Tian, 2011b: Risk assessment of agricultural drought using the CERES-Wheat model: A case study of Henan Plain, China. Climate Res., 50, 247256, https://doi.org/10.3354/cr01060.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H. Y., G. Gao, W. An, X. Zou, H. Li, and M. Hou, 2017: Timescale differences between SC-PDSI and SPEI for drought monitoring in China. Phys. Chem. Earth, 102, 4858, https://doi.org/10.1016/j.pce.2015.10.022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H. Y., and Coauthors, 2021: Temporal and spatial characteristics of drought in China under climate change. Chin. J. Agrometeor., 42, 6979.

    • Search Google Scholar
    • Export Citation
  • Zhou, D., and Coauthors, 2014: SPEI-based intensity characteristics and cause analysis of drought in north China during recent 50 years. J. Nat. Disasters, 23, 193202.

    • Search Google Scholar
    • Export Citation
  • Zhou, K., and Y. Wang, 2020: Temporal and spatial distribution of multi-scale drought in North China. IOP Conf. Ser.: Earth and Environ. Sci., 428, 012057, https://doi.org/10.1088/1755-1315/428/1/012057.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zou, X. K., and Q. Zhang, 2008: Preliminary studies on variations in droughts over China during past 50 years. J. Appl. Meteor. Sci., 19, 679687.

    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    Spatial distribution of 494 stations used in the study.

  • Fig. 2.

    The average drought intensity trends in North China from 1961 to 2019.

  • Fig. 3.

    The variation in trends of drought in North China in (a) 1961–2019, (b) stage I, (c) stage II, and (d) stage III.

  • Fig. 4.

    The drought intensity trends at typical stations from 1961 to 2019.

  • Fig. 5.

    Patterns of average drought intensity in North China in (a) 1961–2019, (b) stage I, (c) stage II, and (d) stage III.

  • Fig. 6.

    The frequency patterns of severe drought in North China in (a) 1961–2019, (b) stage I, (c) stage II, and (d) stage III.

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