The analysis of annual precipitation evolution characteristics is of great value and significance for revealing the spatial and temporal variation patterns of regional precipitation, water resources development and utilization, short-term climate, drought, flood disaster prediction, etc. The Mann-Kendall (MK) mutation test, cumulative distance level method, and Morlet wavelet analysis were used to analyze the precipitation evolution in Anhui Province from 1961 to 2020. The results showed that the average annual temperature and precipitation in Anhui Province showed a significant increasing trend during 1961–2020, with warming and humidification. 1994 was the year of abrupt climate change in Anhui Province, and the temperature after the abrupt change was 2.10 times that before the abrupt change. El Niño-Southern Oscillation (ENSO) has a synchronized resonance cycle with droughts and floods in Anhui Province at 5.8 a. The annual scale of ENSO events is an important theoretical support for regional drought and flood warnings. The chance of drought and flooding in Anhui Province is greater than 50% in the year of ENSO event or two years after the event, and the year of ENSO event or the year after is prone to drought and flooding, so we should strengthen the flood and drought warning, disaster prevention and mitigation.

  • Changes in precipitation characteristics and changes in droughts and floods were analyzed from the provincial scale.

  • Anhui Province tends to be warm and humid after the sudden change of temperature.

  • The frequency of droughts and floods increases after sudden changes in temperature.

  • Droughts and floods are one of the most frequent and serious meteorological disasters in Anhui Province.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Changes in the hydrological cycle can affect society and the environment (Fang et al. 2017). Precipitation is a key hydroclimatic variable that directly affects floods, droughts, and water resources, and understanding precipitation is key to understanding the complex variability of the hydroclimatic system (Tian et al. 2016). Therefore, a detailed study of regional precipitation's spatial and temporal variability is necessary. Previous studies have found that climate extremes are occurring with increasing frequency worldwide (Lehmann et al. 2015). Hydroclimatic trends are not uniform in time and space, with significant increases in precipitation in central and northern Asia, North America (Barros et al. 2008), and eastern South America (Zhai et al. 2005) and significant decreases in Central and South Asia. Wang et al. (2012) found a gradual upward trend of precipitation in eastern Asia through numerical simulations and pointed out that climate warming increases precipitation. Precipitation is influenced by global warming, and the precipitation characteristics of the basin have changed considerably (Chen et al. 2018). The hydrological cycle processes such as regional precipitation, evaporation, and runoff have also changed with global warming. Floods caused by a significant increase in the number of extreme precipitation events in the region due to climate change have posed a great threat to the safety of people's lives and property. In recent years research on the analysis and prediction of the evolution characteristics of precipitation has gradually become a hot topic (Zong & Zhang 2011; Chen et al. 2019; Huang et al. 2019).

Perry et al. (2020) studied the influence of precipitation on climate extremes in India, and this study provided a basis for decision-making for local managers in India. Koyama & Stroeve (2015) analyzed the changes in the evolutionary characteristics of precipitation in the Arctic Ocean using monthly precipitation data. Aydin & Raja (2020) studied the changes in precipitation's spatial and temporal characteristics in the Arvind region of northeastern Turkey. They analyzed heavy precipitation and drought events to provide technical support for their governmental decisions. Haslinger et al. (2019) conducted a spatial clustering analysis of precipitation time series in the Greater Alps region of Europe to derive the pattern of changes in its evolutionary characteristics. Zhang et al. (2020) studied precipitation and temperature in Heilongjiang province. They concluded that changes in large-scale atmospheric circulation patterns were attributed to the temperature rise in the Arctic and its surrounding areas. Li et al. (2012) applied SPI to analyze precipitation data from 35 meteorological stations in the Huaihe River basin to study its drought index. Li et al. (2021) combined ENSO and Pacific Decadal Oscillation (PDO) to investigate and study precipitation in the Yangtze River basin, suggesting that creating more REPE scenarios may be more beneficial for prediction and water resource management. Han et al. (2021) investigated long-term temperature and precipitation, suggesting that multiple circulation systems influence precipitation. Yan et al. (2014) used the SPI index, P-III curve to determine the flood and drought years 1961–2010 in the Huaihe River basin. They analyzed their evolution patterns based on monthly precipitation data. Ma et al. (2020) analyzed the extreme precipitation index and its spatial and temporal distribution characteristics of the Qinghai-Tibet Plateau from 1961 to 2017, which provided data support for the development of regional disaster prevention and mitigation countermeasures.

In East China, water scarcity and flooding are prominent constraints affecting economic development in Anhui Province. A better understanding of precipitation variability at the regional scale would help determine water resources management policies. However, there is a lack of research combining precipitation characterization and flooding analysis in Anhui Province. The research of this paper is structured as follows: (1) This paper analyzes the trend of temperature and precipitation changes in Anhui Province in the past 60 years based on data from 20 meteorological stations in the period 1961–2020. (2) The temporal and spatial variations of their precipitation occurrence are expressed using MK analysis and kriging interpolation. (3) We use Pa (percentage of precipitation distance level) and Standardized Precipitation Index (SPI) indices for drought and flood classification and combine them with power spectrum analysis and ENSO events to analyze drought and flood disaster cycles in Anhui Province, which will provide a theoretical basis for studying floods and future climate change.

Study area and data

Anhui Province is located in the eastern part of China, with longitude 114 °54′—119 °37′E and latitude 29 °41′-34 °38′N. It spans three major basins: Xin'an River, Huai River, and Yangtze River. The topography is complex and diverse, with mountains, hills, and plains distributed north and south. The weather changes dramatically in the transition zone between the subtropical and warm-humid zones, and natural disasters occur frequently. The regional overview map is shown in Figure 1. Anhui Province has a multi-year average temperature of 15.16 °C and average annual precipitation of 1,194.65 mm. As an important grain-producing region, Anhui Province is greatly affected by temperature and precipitation.

Figure 1

Location of Anhui and distribution of 20 meteorological stations in this study.

Figure 1

Location of Anhui and distribution of 20 meteorological stations in this study.

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The meteorological data (precipitation and temperature, ENSO event statistics) were obtained from the National Meteorological Information Center of the China Meteorological Administration (http://data.cma.cn). They passed a relatively strict data quality control. Considering the update and completeness of the data, if the percentage of missing data exceeded 0.2% (Xu et al. 2018), they were excluded. We prioritized the nearest station data for other stations with missing data and performed linear regression every five years for gap filling of missing data. Among the 20 meteorological stations (Table 1), only Qimen and Chuzhou meteorological stations were missing one year of data. Therefore, we used linear regression of data from Tunxi and Dingyuan stations, the nearest stations measured, to replace them.

Table 1

List and a brief description of meteorological stations used in this study

No.StationLatitude (N)Longitude (E)Number of years of missing dataNo.StationLatitude (N)Longitude (E)Number of years of missing data
Tunxi 29.72° 118.28° 11 Huoshan 31.40° 116.32° 
Qimen 29.85° 117.72° 12 Luan 31.73° 116.50° 
Huangshan 30.13° 118.15° 13 Chuzhou 32.35° 118.25° 
Ningguo 30.62° 118.98° 14 Dingyuan 32.53° 117.67° 
Tongling 30.98° 117.85° 15 Bengbu 32.85° 117.30° 
Wuhuxian 31.15° 118.58° 16 Shouxian 32.43° 116.78° 
Maanshan 31.70° 118.57° 17 Fuyang 32.90° 115.83° 
Chaohu 31.58° 117.83° 18 Suzhou 33.63° 116.98° 
Hefei 31.78° 117.30° 19 Mengcheng 33.27° 116.52° 
10 Tong Cheng 31.07° 116.95° 20 Bozhou 33.87° 115.77° 
No.StationLatitude (N)Longitude (E)Number of years of missing dataNo.StationLatitude (N)Longitude (E)Number of years of missing data
Tunxi 29.72° 118.28° 11 Huoshan 31.40° 116.32° 
Qimen 29.85° 117.72° 12 Luan 31.73° 116.50° 
Huangshan 30.13° 118.15° 13 Chuzhou 32.35° 118.25° 
Ningguo 30.62° 118.98° 14 Dingyuan 32.53° 117.67° 
Tongling 30.98° 117.85° 15 Bengbu 32.85° 117.30° 
Wuhuxian 31.15° 118.58° 16 Shouxian 32.43° 116.78° 
Maanshan 31.70° 118.57° 17 Fuyang 32.90° 115.83° 
Chaohu 31.58° 117.83° 18 Suzhou 33.63° 116.98° 
Hefei 31.78° 117.30° 19 Mengcheng 33.27° 116.52° 
10 Tong Cheng 31.07° 116.95° 20 Bozhou 33.87° 115.77° 

Trend analysis

The cumulative distance level method (Wang et al. 2019) was used for the analysis of precipitation variation characteristics. A rising cumulative distance level curve indicates that the distance level value increases and the precipitation is on an upward trend. On the contrary, the distance level decreases, and the precipitation tends to decrease. From the ups and downs of the curve, we can visually determine the characteristics of precipitation changes at different stages in the long-term evolution. Let a time series be , then its cumulative distance level at the moment of t time can be expressed as (Yang et al. 2020).
(1)
where,

Mann-Kendall (MK) mutation test

The MK mutation test is a nonparametric statistical test that has the advantage of being less demanding on the sample, is not disturbed by a few outliers, is more suitable for ordinal and type variables, and is simpler to calculate. It can be tested for the time of occurrence, number of mutations, and significance of mutations (Yacoub & Tayfur 2020). It is used to analyze the mutation points of annual average temperature and precipitation in Anhui Province.

For the time series X1-Xn, construct:
(2)
where Ri denotes the cumulative number of Xi greater than Xj (1 ≤ j ≤ i). Under the assumption that the time series are randomly independent, the statistic is defined as:
(3)
where UF1 = 0 when k = 1. Var(Sk) is the mean and variance of the cumulative number Sk, and , .

In the test process, the positive series curve UF exceeds the 95% threshold of significant test value in the case that there is only one obvious intersection point between UF and UB, and at the same time the point lies within the two critical lines, indicating that the point is a mutation point and is significant in statistical significance (Wang et al. 2017). If there are multiple intersection points within the confidence interval, all of these points are considered as possible mutation points. Conversely, if the intersection point lies outside the critical line, this mutation point is considered insignificant.

Periodic analysis

Wavelet analysis involves wavelet transform and wavelet function, which is a high resolution and can analyze the evolution in time series, mostly used in hydrological series time scale analysis. The wavelet function is oscillatory and can decay to zero quickly.

For any function , ( denotes the square productable space, denotes the set of real numbers, its continuous wavelet transform (Wu & Guo 2021) is:
(4)
where, is the wavelet coefficient. a is the periodic scale factor. is the complex conjugate function.
The wavelet variance is obtained by integrating the continuous wavelet transform coefficients and is the main parameter reflecting the time scale of the series, which is calculated as:
(5)
There are more commonly used wavelet functions, such as MexicanHat, Morlet wavelet and so on. Among them, Morlet wavelet constant is used for the period analysis of hydrological time series and has the best effect, and its specific calculation formula is:
(6)
where, is a constant. i is the imaginary unit.

Drought and flood classification

The index can directly reflect the drought caused by precipitation anomalies, and its calculation is easy and has been widely used in the meteorological analysis in China (Ashraf & Routray 2015; Xiao et al. 2021). Its calculation formula is as follows:
(7)
where, is the precipitation distance level percentage, is the annual average precipitation of a year, is the average precipitation of the study period. The index drought classification table is available in China, see Table 2.
Table 2

index drought grade classification

PaDrought level
Pa < −40% Severe drought 
− 40% ≤ Pa <− 30% Moderate drought 
− 30% ≤ Pa <− 15% Light drought 
Pa ≥ −15% No drought 
PaDrought level
Pa < −40% Severe drought 
− 40% ≤ Pa <− 30% Moderate drought 
− 30% ≤ Pa <− 15% Light drought 
Pa ≥ −15% No drought 
Table 3

SPI index drought and flood grade classification

SPIDrought and flood levels
SPI < −2.0 Severe drought 
− 1.99 < SPI < −1.5 Moderate drought 
− 1.49 < SPI < −1.0 Light drought 
1.0 < SPI < 1.49 Light waterlogging 
1.5 < SPI < 1.99 Moderate waterlogging 
SPI > 2.0 Heavy waterlogging 
SPIDrought and flood levels
SPI < −2.0 Severe drought 
− 1.99 < SPI < −1.5 Moderate drought 
− 1.49 < SPI < −1.0 Light drought 
1.0 < SPI < 1.49 Light waterlogging 
1.5 < SPI < 1.99 Moderate waterlogging 
SPI > 2.0 Heavy waterlogging 
SPI can characterize the lack of precipitation in different time periods in a quantitative way. The use of SPI index (Table 3) as a part of comprehensive evaluation for drought detection has also been widely used internationally (Wang et al. 2015).
(8)
where, , and are the scale and shape parameters, respectively, which are greater than zero.
(9)
where n is the length of the precipitation sequence, where the term that is zero is m. is the average of the non-zero terms in the precipitation sequence.

The flow chart is shown in Figure 2.

Figure 2

Flow chart of precipitation and flooding analysis in Anhui Province.

Figure 2

Flow chart of precipitation and flooding analysis in Anhui Province.

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Analysis of interannual variation of precipitation characteristics

The fitted polynomial for the average annual precipitation in Anhui Province during 1961–2020 was y = 0.0767x2 − 302.75x + 299872 with R2 of 0.0681 (Figure 3), and the results passed the significance check at the 95% level after the significance analysis by SPSS software (Tasnim et al. 2021). The multi-year average precipitation in Anhui Province was 1,194.65 mm, of which the extreme value occurred in 2016 with 1,661 mm and the minimal value was 719 mm in 1973. The climate of Anhui Province tends to be warm and humid overall in the last 60 years, and this conclusion is consistent with many research results, warming and humidifying (Liu & Xu 2016; Song et al. 2019) trend characteristics.

Figure 3

Annual precipitation changes in Anhui Province from 1961 to 2020.

Figure 3

Annual precipitation changes in Anhui Province from 1961 to 2020.

Close modal

The MK test was used to analyze the precipitation in Anhui Province from 1961 to 2020, and the results are shown in Figure 4. Between the confidence intervals, there are more intersections of UF and UB lines, such as in 1976, 1984, 1994, and 2014, indicating that there is a possibility of sudden changes in precipitation in all these years. However, the UF curve has been showing an increasing trend since 2014, which indicates a significant increase in precipitation after 2014. Meanwhile, the data from 20 meteorological stations were tested for trend analysis and the results were expressed by kriging interpolation, and the results are shown in Figure 5. The test value is greater than the threshold value of 1.96 in the southern, as well as the southeastern regions of Anhui Province, which shows a significant difference in precipitation variation, and conversely, most of the northern as well as northwestern regions do not exceed 1.96, indicating that there is no significant variation trend.

Figure 4

1961–2020 MK test results of precipitation in Anhui Province.

Figure 4

1961–2020 MK test results of precipitation in Anhui Province.

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Figure 5

Interpolation of precipitation trends in Anhui Province from 1961 to 2020.

Figure 5

Interpolation of precipitation trends in Anhui Province from 1961 to 2020.

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In order to further reveal the characteristics of regional rainfall abrupt changes, the cumulative distance level is used to analyze the abrupt years of precipitation trends in Anhui Province from 1961 to 2020.

The cumulative distance level is an effective method to judge the climate change pattern. The cumulative distance level value of precipitation for consecutive years is used to draw the cumulative distance level curve of multi-year precipitation in Anhui Province, so as to analyze the trend of precipitation characteristics and the continuous change pattern for many years. Secondly, it is possible to determine the years of sudden changes in precipitation. When the cumulative distance level shows a decreasing trend, it means that the period is in a period of less rain, and vice versa, it is in a period of more rain. The cumulative distance level curves of annual precipitation series in Anhui Province was plotted for analysis (Figure 6). From Figure 6, the annual precipitation in Anhui Province can be divided into three stages. 1961–1968 is the stage of low rainfall, in which the overall distance level shows negative values and the cumulative distance level curve decreases, and the precipitation in this period is the lesser stage in 60 years with a shorter duration. 1967–2013 is the oscillation stage, in which the frequency of positive and negative distance levels is not very different and the cumulative distance level curve fluctuates at 1,200 mm. After 2014, the overall distance level curve showed a positive distance level, and the cumulative distance level curve increased significantly, and the annual precipitation entered the stage of high. The average annual precipitation from 1961 to 2014 was 1,171.19 mm/a before the sudden change in precipitation, and the average precipitation after the sudden change was 1,623.18 mm/a. The precipitation after the sudden change was 1.39 times of the previous one and 1.36 times of the multi-year average precipitation.

Figure 6

Annual precipitation accumulation distance level change in Anhui Province.

Figure 6

Annual precipitation accumulation distance level change in Anhui Province.

Close modal

Morlet wavelets were used to analyze the cycle evolution of annual average precipitation variation data in Anhui Province. The real contours of wavelet transform coefficients and their variance plots were plotted (Figure 7).

Figure 7

The Morlet wavelet transform and the wavelet variances of annual mean precipitation in Anhui Province during 1961–2020.

Figure 7

The Morlet wavelet transform and the wavelet variances of annual mean precipitation in Anhui Province during 1961–2020.

Close modal

From the variogram, it can be seen that there are three extreme points in the variance curve of annual precipitation in Anhui Province, which indicates that there are three main cycles of annual average precipitation in Anhui Province. The first main cycle is 28a, the second main cycle is 9a, and the third main cycle is 3a. The evolution of annual precipitation changes over the three main cycles can be analyzed by combining the wavelet coefficients with the real part of the isogram. In the time scale of the first main cycle 28a, the precipitation experiences the evolution of abundant-depleted-abundant-depleted-abundant-depleted, and up to 2020, the precipitation is in the state of partial abundance. According to the analysis of its closed curve distribution, the precipitation will remain in a state of partial abundance for at least the next five years. On the time scale of the second main cycle 9a, it can be seen from the color change of the contour map and the degree of curve closure that the oscillation is strong and stable. In the 3a time scale of the third main cycle, the oscillation energy of the real precipitation contour map decreases. The intensity of the oscillation decreases significantly, and it no longer shows a significant alternating cycle of precipitation.

Analysis of drought and flood disasters

Anhui Province is in the north-south climate transition zone, a prevailing monsoon area, with southeast winds prevailing in summer, warm and humid, and northwest divisions prevailing in winter, cold and dry. The interannual variation rate of temperature is 0.22 °C/10a, which is lower than the warming rate of 0.25 °C/10a in China, slightly lower than China (0.2–0.3 °C/10a) and the east (0.26 °C/10a), and higher than southern China (0.10 °C/10a) and western China (0.18 °C/10a), indicating a statistically significant upward trend in temperature in Anhui. The average annual very high temperature was 16.6 °C in 2018, 2019, and 2020, and the average annual very low temperature was 13.95 °C in 1970. The areas along the Huaihe River and Yangtze River are influenced by waters and lakes and have relatively low elevation, industrial concentration, relatively high population, high energy consumption, and significant urban heat island effect (Zhang et al. 2020), all of which contribute to the rise in temperature.

The signal-to-noise ratio can measure the difference in temperature or precipitation with different period statistics. It is the ratio of the sum of the absolute value and its standard deviation derived from the difference between the mean values of precipitation or temperature at two stages before after the year of abrupt change. This can further verify the authenticity of the abrupt climate change. If the ratio is greater than 1, the abrupt climate change is considered to exist, and vice versa, the abrupt change is considered insignificant. The signal-to-noise ratio S/N is calculated as follows.
(10)
where, , and , represent the mean and standard deviation of the temperature in the two phases before and after the turning year, respectively.

Figure 8 shows the changes in annual average temperature and precipitation in Anhui Province from 1961 to 2020. According to the change of the cumulative distance level value of annual average temperature in Anhui Province, it can be seen that the cumulative distance level curve gradually increases after 1994, and the rate is faster. The signal-to-noise ratio of 1994 is 1.01 > 0, which indicates a sudden climate change. Thus, 1994 can be identified as the sudden climate change point. The precipitation change rate from 1961 to 1994 before the abrupt temperature change was 62.41 mm/10a, while the precipitation change rate after the abrupt change (1995–2020) surged to 130.79 mm/10a, which was 2.10 times higher than that before the abrupt temperature change.

Figure 8

1961–2020 changes in annual average temperature and precipitation in Anhui Province.

Figure 8

1961–2020 changes in annual average temperature and precipitation in Anhui Province.

Close modal

To reflect the effect of sudden temperature changes on precipitation changes more obviously, we used the Pa index and SPI index to classify the drought and flood levels and count the changes of drought before and after sudden temperature changes, as shown in Figures 9 and 10. the average precipitation during 1961–1994 was 1,167.27 mm, during which normal years were predominant. Three consecutive years of 1966, 1967, and 1968 had mild drought conditions, and five years of mild drought in 1976, 1986, 1988, 1992, and 1994. Only 1978 was a moderate drought. After the sudden temperature change, the average annual precipitation significantly increased between 1995 and 2020. The number of drought years decreased significantly during this period, with only two years of mild drought in 2001 and 2019. In the 34 years before the abrupt temperature change, there was one moderate drought, eight light droughts, 20 normal years, four light floods, and one moderate flood. The drought years were significantly greater than the flood years, with a drought and flood frequency of 41.18%. In the 26 years after the sudden temperature change, drought decreased significantly, with only two light droughts and 12 normal years. Flood years increased significantly, with five light floods, six moderate floods, and one severe flood, and the frequency of droughts and floods reached 53.85%.

Figure 9

Calculation results of drought and flood levels.

Figure 9

Calculation results of drought and flood levels.

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Figure 10

Changes in frequency of droughts and floods before and after sudden changes in temperature.

Figure 10

Changes in frequency of droughts and floods before and after sudden changes in temperature.

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According to statistics, extreme rainfall caused 95 counties (cities and districts) in 16 cities in Anhui Province, affected 10,465,300 people, 14 people died as a result of the disaster, 1,238,800 people were urgently relocated, crop disaster area of 1,221.31 thousand hectares, of which 393.7 thousand hectares of crop failure, the collapse of 5,927 houses, 27,500 severely damaged houses, general damage to houses. The direct economic loss was 60.065 billion yuan, including 19.929 billion yuan of agricultural loss, 8.431 billion yuan of industrial and mining enterprise loss, 24.158 billion yuan of infrastructure loss, 1.988 billion yuan of public welfare facilities loss, and 5.538 billion yuan of household property loss.

Power spectrum analysis can determine the main frequencies of regional drought and flood disaster cycle changes. Power spectrum analysis will be of great help to the prediction and prevention of drought and flood disasters. The power spectrum analysis shows about a 5.8a oscillation cycle of drought and flood disasters in Anhui Province during 1961–2020 (Figure 11). The study shows that ENSO events are correlated with regional droughts and floods and are one of the main driving factors triggering temperature and precipitation changes. Most of China is wet under ENSO cold state disturbances, and the opposite is true for ENSO warm states. 2–7a main cycles of ENSO events are observed, indicating more synchronized resonance cycles between drought and floods and ENSO events in Anhui Province.

Figure 11

Power spectrum cycle analysis of drought and flood disasters in Anhui Province from 1961 to 2020.

Figure 11

Power spectrum cycle analysis of drought and flood disasters in Anhui Province from 1961 to 2020.

Close modal

To better characterize the relationship between ENSO events and floods, it is necessary to count and determine the comparative relationship between ENSO events and drought and flood occurrence years during 1961–2020. Plotting the relationship between precipitation and ENSO events (Figure 12). During these 60-year period, there were 31 drought and flood events, 18 El Niño warm events, and 13 La Niña cold events. Among them, 11 droughts and floods occurred in the year of the warm event, of which (5 floods and 6 droughts), the chance of droughts and floods occurring in El Niño years was 61.11%. In the year of the cold event, 7 droughts and floods occurred (3 floods and 4 droughts), and the chance of droughts and floods occurring in a La Niña year is 53.85%. Two years after the El Niño warm event, 10 droughts and floods (3 floods and 7 droughts) occurred, with a 55.56% chance. Two years after the La Niña event, there were 8 droughts and floods (4 droughts and 4 floods), with a 61.54% chance. In the year of the ENSO event or two years later, the chance of droughts and floods is greater than 50%. The year of the ENSO event or the year after is prone to droughts and floods, and we suggest that we strengthen flood and drought warning, disaster prevention, and mitigation.

Figure 12

1961–2020 precipitation in relation to El Niño and La Niña events.

Figure 12

1961–2020 precipitation in relation to El Niño and La Niña events.

Close modal
  • (1)

    The average annual temperature and precipitation in Anhui Province during 1961–2020 show a significant upward trend with a tendency of warming and humidification. 2014 is the year of precipitation abrupt change, and the average precipitation before the abrupt change (1961–2014) is 1,171.19 mm/a, and the average precipitation after the abrupt change is 1,623.18 mm/a. The precipitation after the abrupt change (2015–2020) is 1.39 times before the mutation and 1.36 times the multi-year average precipitation. There are three main cycles of annual average precipitation in Anhui Province. Among them, the first main cycle is 28a, the second main cycle is 9a, and the third main cycle is 3a. up to 2020, the precipitation in Anhui Province is still in a partial abundance, and the precipitation will remain in a partial abundance for at least the next five years. 1994 is a year of abrupt climate change in Anhui Province, and the precipitation change rate before the abrupt temperature change (1961–1994) is 62.41 mm/10a, while the precipitation change rate after the abrupt change (1995–2020) is 62.41 mm/10a. Before the sudden temperature change (1961–1994), the precipitation change rate was 62.41 mm/10a, but after the sudden change (1995–2020), the precipitation change rate increased to 130.79 mm/10a, which was 2.10 times of that before the sudden temperature change. After the abrupt temperature change, droughts and floods show an increasing trend, and extreme drought and flood events are more frequent.

  • (2)

    ENSO events and droughts and floods in Anhui Province have a more synchronous resonance cycle at 5.8a. ENSO events are important theoretical support for regional drought and flood warnings at the annual scale. The chance of droughts and floods in Anhui Province is greater than 50% in the year of ENSO event or two years after the event, and the year of ENSO event or the year after the event is prone to droughts and floods, so we should strengthen flood and drought warning, disaster prevention and mitigation.

  • (3)

    This paper only analyzes the precipitation characteristics based on temperature and precipitation data. The subsequent study will further investigate the influence mechanism of drought and flooding by combining other climatic factors such as atmospheric circulation and subsurface conditions.

All authors contributed to the study's conception and design. Writing and editing: Xianqi Zhang and Dong Zhao; preliminary data collection and chart editing: Yue Zhao and Yihao Wen; all authors read and approved the final manuscript.

This work was supported by the Key Scientific Research Project of Colleges and Universities in Henan Province (CN) [grant numbers 17A570004].

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Not applicable.

Not applicable.

Not applicable.

All relevant data are included in the paper or its Supplementary Information.

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