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Article

Factors Affecting the Spatiotemporal Variation of Precipitation in the Songhua River Basin of China

by
Zhijun Li
1,2,
Hongnan Yang
1,2,* and
Minghui Jia
1,2
1
School of Hydraulic and Electric-Power, Heilongjiang University, Harbin 150080, China
2
Institute of Groundwater in Cold Regions, Heilongjiang University, Harbin 150080, China
*
Author to whom correspondence should be addressed.
Water 2024, 16(1), 2; https://doi.org/10.3390/w16010002
Submission received: 23 November 2023 / Revised: 14 December 2023 / Accepted: 16 December 2023 / Published: 19 December 2023
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Abstract

:
The study aimed to investigate the spatiotemporal variation of annual precipitation and extreme precipitation within the Songhua River Basin (SRB). It utilized precipitation data collected from 60 meteorological stations within the SRB during the period 1968–2019. Employing Empirical Orthogonal Function (EOF) analysis, it decomposed spatiotemporal characteristics of annual precipitation in the SRB. Through Pearson correlation analysis, application of the cross-wavelet transform, and wavelet coherence analysis, the current study explored the correlation between geographical factors, local air temperature, circulation factors, and annual and extreme precipitation. The results indicated an increasing trend for annual precipitation and for most indices of extreme precipitation within the SRB, apart from the consecutive dry days (CDD). Spatially, a general pattern of “more in the east and less in the west” was observed. Annual precipitation types in the basin were resolved into two modes with the first mode showing a general tendency of more (or less) precipitation over the entire basin, while the second mode exhibited less (or more) precipitation in the western areas and more (or less) in the eastern areas. Longitude, latitude, and altitude significantly impacted annual precipitation and extreme precipitation. Local air temperature notably affected the consecutive wet days (CWD). The West Pacific Subtropical High (WPSH) exerts a strong influence on the annual precipitation and extreme precipitation within the basin.

1. Introduction

One of the significant elements in the hydrological cycle is precipitation [1]. Over the years, the global warming phenomenon has become increasingly severe, leading to an increase in extreme precipitation events, which have a substantial impact on various facets of society, the economy, and the ecological environment [2,3,4]. Numerous studies have examined the spatiotemporal variation of precipitation and extreme precipitation [5,6,7]. Precipitation and the intensity, frequency, and duration of extreme precipitation have been observed to be on the rise in several countries and regions worldwide [8,9]. Whether in humid or arid regions, extreme precipitation has a substantial upward trend [10]. Despite the upward trends observed for mean annual and extreme precipitation for most of the world [11,12], there are still regions where it shows no trend or even a downward trend [13]. Mathbout et al. [14] showed a notable decline in annual precipitation in the southern section of the Eastern Mediterranean region from 1961 to 2012. They also observed a notable decline in extreme precipitation throughout the entire region. Hence, it is crucial to perform a study specifically on precipitation and extreme precipitation of local regions [15].
China has complex terrain and diverse climate types. The spatiotemporal variation tendencies for annual and extreme precipitation exhibit differences for different regions of China [16,17]. Guan et al. [18] studied the precipitation in the Yangtze River Basin during 1960–2012. They found that most of the indices for extreme precipitation showed an increasing trend, while only R10mm (total annual days when daily precipitation is more than 10 mm) and CWD (maximum of consecutive days when daily precipitation is more than 1 mm) showed a significant decreasing trend, and the regions with more precipitation were mainly located in the middle and lower sections of the Yangtze River and the eastern portion of the Tibet Plateau. Li et al. [19] examined extreme precipitation that occurred in desert regions of China during 1960–2018. Indices for extreme precipitation exhibited a rising trend for most desert areas. Moreover, the eastern deserts were drier than the western deserts. Zhao et al. [20] explored the spatiotemporal variations of mean and extreme precipitation in the Yellow River Basin during 1961–2016. Their results revealed that the annual average precipitation in both the entire basin and the middle and lower sections of the Yellow River exhibited a decreasing trend during the study period. They also observed that heavy rainfall days exhibited a decrease before 2013, and the decreasing trend was alleviated after 2013. Given the spatiotemporal variability of mean and extreme precipitation climatology, several studies have analyzed the reasons for the observed differences [21,22,23,24,25], including geographical factors [26,27,28,29], global warming [30,31,32], circulation factors [11,33,34,35,36], and urbanization [37,38].
The SRB serves as a crucial grain production area in China with abundant natural resources of high ecological and economic value. Floods in the SRB are usually caused by heavy rainfall. Droughts and floods have threatened sustainable economic development and ecological security in the basin. Several studies have evaluated mean annual and extreme precipitation within the SRB [39,40,41,42,43,44]. Compared with previous studies, here, we comprehensively study the precipitation variations within the SRB in recent years. Few previous studies have been undertaken on the factors affecting precipitation within the SRB. Most of them focused on the influence of atmospheric circulation factors [44]. We have conducted a more comprehensive study of the influence of geography, local air temperature, and circulation factors including atmosphere and ocean on the annual and extreme precipitation within the SRB. This paper analyzes the spatiotemporal variation of mean annual and extreme precipitation, utilizing precipitation data from meteorological stations within the SRB during the period 1968–2019. It also examines factors that potentially influence mean annual and extreme precipitation throughout the SRB. Improved understanding of precipitation processes supports water resource planning and management and the development of flood risk reduction measures within the SRB.

2. Study Area, Data and Method

2.1. Overview of the Study Area

The SRB is located in northeastern China, with an area of 5.57 × 105 km2 (Figure 1). It is contained within the box region defined by 119°52′, 132°31′ E × 41°42′, 51°38′ N. The SRB covers the provinces of Liaoning, Jilin, Heilongjiang, and Inner Mongolia Autonomous Region. The basin is enclosed by mountains on three sides, with the Changbai Mountain in the southeast, the Daxing’anling area in the west, and the Xiaoxing’anling area in the northeast. The central area consists of the vast Songnen Plain, which serves as a significant agricultural zone in China. This basin falls within the temperate monsoon climate zone, characterized by four distinct seasons and significant temperature variations throughout the year.

2.2. Data

This study utilized daily precipitation data from 60 meteorological stations within the SRB during the period 1968–2019. Annual precipitation totals were computed at each station. The annual precipitation of the basin was calculated using the Thiessen Polygon Method [45]. The data for precipitation was supplied by the China Meteorological Data Service Centre (https://data.cma.cn/, accessed on 12 June 2023). In addition, seven indices of extreme precipitation directed at intensity, duration, and frequency were also computed [46] (Table 1). The RclimDex 1.0 (https://etccdi.pacificclimate.org/software.shtml, accessed on 29 October 2023) based on R 4.3.1 software (https://www.r-project.org/, accessed on 29 October 2023) was used to compute the extreme precipitation indices.
This study evaluated three geographical factors: latitude, longitude, and altitude. They were sourced from Resource and Environment Science and Data Centre of Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 10 June 2023). Three air temperature indices are considered: the average annual temperature, the annual average maximum temperature, and the annual average minimum temperature in the SRB. The data for temperature was supplied by the China Meteorological Data Service Centre (https://data.cma.cn/, accessed on 12 June 2023). Several circulation factors, acquired from the National Oceanic Atmospheric Administration Climate Prediction Center (https://psl.noaa.gov/data/climateindices/list/, accessed on 31 August 2023), were selected for evaluation, including the North Atlantic Oscillation (NAO), Southern Oscillation Index (SOI), Arctic Oscillation(AO), Pacific Decadal Oscillation (PDO), Western Pacific Index (WP), Extreme Eastern Tropical Pacific SST (Niño 1+2), Eastern Tropical Pacific SST (Niño 3), Central Tropical Pacific SST (Niño 4), and the East Central Tropical Pacific SST (Niño 3.4). The impact of the Western Pacific Subtropical High on the climate in China is also very important [47]. Hence, we selected the Western Pacific Subtropical High Intensity (WPSHI) index and the Western Pacific Subtropical High Area (WPSHA) index, which were sourced from the National Climate Centre of China (http://cmdp.ncc-cma.net/cn/prediction.htm#pred, accessed on 31 August 2023).

2.3. Methods

This paper used linear trend fitting and the Mann–Kendall trend test to determine the trend variation for annual precipitation and each index of extreme precipitation. To use the cumulative anomaly method [48] is to accumulate the difference between each value of the time series and their average value to obtain a new time series. A curve of the cumulative anomaly values is then plotted, the inflection point of which can be determined as a mutation point. The Pettitt test [49,50] is a non-parametric test method, which can test whether there are mutation points in a certain element of the time series. Both methods were used to identify potential mutation points in this paper. By combining the points obtained by the two methods, the final mutation point could be determined. Ordinary Kriging was selected to spatially interpolate the annual precipitation and indices of extreme precipitation throughout the SRB. Empirical Orthogonal Function (EOF) decomposition [51,52,53], also known as principal component analysis, is a method to analyze the structural features in matrix data and extract the main feature vectors. This method was utilized to decompose the spatial modes for annual precipitation within the SRB and extract the spatiotemporal characteristics of meteorological elements. The North criterion [54] was employed to determine the precipitation field types, with the North criterion’s testing standard being that the error limits of the modal eigenvalues should not overlap.
The Pearson correlation method [55,56] can be used to measure the degree of linear correlation between data. To analyze the factors that affect annual precipitation and extreme precipitation, this method was utilized to identify the relationship between geographical factors, local air temperature, circulation factors, and annual and extreme precipitation indices. The study also examined the interaction between the spatial modal patterns of annual precipitation and circulation factors. The cross-wavelet transform (XWT) [57,58] is used to analyze the correlation between two sequences in the high-energy zones. The wavelet coherence analysis (WTC) [59] is used to analyze the correlation between two sequences in the low-energy zones, and is capable of examining the local correlation between them. Both were applied to analyze periodic connections between precipitation and circulation factors in this paper. These methods help identify the relationships and potential interactions between precipitation within the SRB and various factors.

3. Results

3.1. Analysis of Temporal Variation

3.1.1. Trend Analysis

The Mann-Kendall trend test was conducted on the annual precipitation and indices for extreme precipitation, and the outcomes of the linear trend fitting are shown in Figure 2. From the Z-values of the Mann-Kendall trend test, it was observed that, except for CDD, which shows an overall decreasing trend, the remaining indices for extreme precipitation and annual precipitation all exhibited an increasing trend. Specifically, annual precipitation, R20mm, and R25mm were found to be statistically significant at the 0.1 level; R95p and R99p were found to be statistically significant at the 0.05 level; R10mm, CWD, and CDD show no statistical significance about trend changes. As shown in Figure 2, the findings of the linear trend fitting align with those of the Mann–Kendall trend test.

3.1.2. Mutation Analysis

Figure 3 shows the anomaly and cumulative anomaly of annual precipitation and the seven indices of extreme precipitation within the SRB. As shown in Figure 3, the cumulative anomaly trends for annual precipitation, R10mm, R20mm, R25mm, R95p, and R99p exhibited a pattern of “decrease-increase-decrease-increase”, overall presenting a “W” shaped variation. The annual precipitation and R10mm showed a decreasing trend from 1968 to 1982, indicating a period of relatively low precipitation. From 1983 to 1998, there was an upward trend, with a particularly noticeable increase in precipitation in 1998. From 1999 to 2011, there was an overall decreasing trend, reaching its lowest point in 2011. From 2012 to 2019, there was an upward trend, indicating a period of relatively high precipitation. The cumulative anomaly curves of annual precipitation (Figure 3a) and R10mm (Figure 3b) have three general inflection points. It can be preliminarily inferred that the annual precipitation and R10mm may have undergone mutation in 1982, 1998, and 2011. Similarly, from Figure 3, it can be observed that R20mm and R25mm may have experienced mutation in 1982, 1998, and 2009, while R95p and R99p may have shown mutation in 1984, 1998, and 2011. The cumulative anomaly trend for the extreme index CWD showed a more complex variation. From 1968 to 1979, there was an overall decreasing trend with only 1969 showing CWD values higher than the average. From 1980 to 1988, there was an upward trend, followed by a downward trend from 1989 to 1995. From 1996 to 1998, there was a rapid increase, with the CWD value in 1998 exceeding the average by 2.06 days. From 1999 to 2008, there was a decreasing trend, followed by an upward trend from 2009 to 2019. It can be preliminarily inferred that the extreme precipitation index CWD may have undergone mutation in 1979, 1988, 1995, 1998, and 2008. In contrast with the cumulative anomaly trends for the other extreme precipitation indices, the cumulative anomaly trend for the extreme precipitation index CDD exhibited a pattern of “increase-decrease-increase-decrease”. It can be preliminarily inferred that it may have undergone mutation in 1984, 1990, and 2003.
Through the cumulative anomaly method, identification of the mutation points can be made. However, to more accurately determine the mutation points, the Pettitt test may be conducted as the next step. As depicted in Figure 4, the mutation points for annual precipitation and R10mm were in 2011; for R20mm and R25mm, they were in 2009; for CWD, it was in 1979; for CDD, it was in 2003; for R95p, they were in 1982 and 1984; and for R99p, it was in 1983. Except for CWD for which the significance level (αt) was greater than 0.5, the significance levels for the rest of the indices were generally between 0.05 and 0.5, indicating that the mutation points were effective but not significant. By combining the cumulative anomaly method and Pettitt test, the final mutation points are presented in Table 2.

3.2. Analysis of Spatial Variation

3.2.1. Spatial Distribution

The spatial distribution for mean annual precipitation and the means for each of the seven extreme precipitation indices within the SRB were computed using a geographic information system (Figure 5). Apart from CDD, the spatial distribution of mean annual precipitation and the means for the remaining extreme precipitation indices were similar, with high-value areas mainly centered in the zone of Changbai Mountain in the southeast and the zone of Xiaoxing’anling in the northeast. The areas with low values were predominantly concentrated in the west and gradually decreased from northwest to southwest. Characteristics of the spatial distribution for the extreme precipitation index CDD were opposite to the others, mainly showing a “more in the west and less in the east” distribution pattern, with higher values in the southwest, indicating a longer duration of drought in the southwest area of the SRB. The high-value areas of the spatial distribution for the CWD index not only included the Changbai Mountains in the southeast and the Xiaoxing’anling areas in the northeast but also covered some parts of the Daxing’anling areas in the northwest. It indicates that these areas have longer periods of consecutive wetness within the SRB. From R10mm to R20mm and then to R25mm (Figure 5b–d), the spatial distribution characteristics are mostly consistent, but it is worth noting that the area centered around the middle area in the western part of the SRB is gradually turning red, indicating that this area is mainly characterized by extremely heavy precipitation throughout the study period.
Regarding the distribution of trend variation for the stations, as shown in Figure 6, except for CDD, an upward trend in both annual precipitation and indices of extreme precipitation is observed in most of the stations. Specifically, 81.6% of the stations exhibit an upward trend in annual precipitation, of which 15 stations pass the significance test. More than 80% of the stations for R10mm, R20mm, and R25mm show an upward trend, signifying a heightened occurrence of extreme precipitation frequency at the majority of stations within the SRB in the study period. Similarly, 61.7% of the stations show an upward trend in CWD, and four stations pass the significance test. In contrast, 75% of the stations show a downward trend in CDD, and nine stations pass the significance test, indicating that many areas in the SRB have an increasing trend of consecutive wet time and a decreasing trend of consecutive dry time year by year. From the perspective of precipitation intensity indicators, 76.7% of the stations show an upward trend in R95p, with ten stations passing the significance test. Meanwhile, 68.3% of the stations show an upward trend in R99p, with six stations passing the significance test. Overall, most areas within the SRB are characterized by an increase in precipitation, a rise in frequent and intense extreme precipitation events, and a prolonged period of wetness.

3.2.2. Empirical Orthogonal Function Analysis

Using the Empirical Orthogonal Function (EOF) analysis method, a spatiotemporal decomposition mode analysis was conducted on the annual precipitation data within the SRB from 1968 to 2019. The North criterion was subsequently applied to determine the distribution types of the precipitation field. According to Table 3, the first six decomposed modes contributed to a cumulative variance rate of 73.55%. Among them, the first two modes passed the North criterion with the cumulative variance contribution rate of 54.36%. This can effectively reveal the spatiotemporal modal variation of annual precipitation within the SRB during the period 1968–2019.
The variance contribution rate of mode 1 was 43.19%, which is higher than that of the other modes. It is the most important spatial modal type, exerting a dominant influence on the annual precipitation fields within the SRB. The distribution of eigenvectors of mode 1 is shown in Figure 7a. The eigenvectors of this mode are all positive values, indicating a consistent tendency for precipitation across the whole basin to remain more or less the same throughout the year. The lower value areas of eigenvectors are located in parts of the Changbai Mountain area in the east, the Daxing’anling area in the northwest, and some areas in the southwest. The areas with high values of eigenvectors include Huerle, Shuangcheng, Shangzhi, and Hegang, the places indicated with darker red in Figure 7a.
The variance contribution rate of mode 2 was 11.17%. The distribution of eigenvectors is shown in Figure 7b. The eigenvectors of this mode have both positive and negative values. With “Yichun-Tieli–Beilin–Qianguo–Changling” as the boundary line, the negative value areas are concentrated in the west, while the positive value areas are concentrated in the east. The areas with high positive values are mainly in the southeast, with Donggang being the highest-value area and Changling being the lowest-value area. The absolute negative values mainly show a decreasing trend from the west to the boundary line, with Zhalantun being the area with the highest absolute negative value and Yichun being the lowest absolute negative value.
The two spatial modes obtained from the EOF analysis indicated that there were four forms of annual precipitation within the SRB during the period 1968–2019. The first mode determines whether the precipitation in the entire basin was generally more or less throughout the year, while the second mode determined whether the precipitation was more in the east and less in the west, or less in the east and more in the west throughout the year.
The time coefficients represent the temporal variation characteristics of the corresponding eigenvector spatial modes. The positive or negative value of the coefficient determines the direction of the mode; positive values signify the same direction, whereas negative values signify the opposite direction. The larger the absolute value of the time coefficient, the more typical the mode is at that moment. The trend of the time coefficient for the first mode is shown in Figure 8a. The trend slope of the time coefficient of mode 1 was greater than 0, suggesting that the precipitation within the SRB had an increasing trend over the 52 years studied. As shown in Figure 8b, the trend slope of the time coefficient of mode 2 was also slightly greater than 0, suggesting that the precipitation in the western part of the SRB had a decreasing trend and the precipitation in the eastern part has an increasing trend in the past 52 years. Table 4 shows the representative years for the four types of precipitation fields. Specifically, for the first mode, there were 16 years with overall more precipitation and 20 years with overall less precipitation. For the second mode, there were 9 years with less precipitation in the western part and more in the eastern part, and 7 years with more precipitation in the western part and less in the eastern part.

3.3. Analysis of Influencing Factors

3.3.1. Geographical Factors

Using the Pearson correlation analysis, the study examined the relationship between precipitation and geographical factors, including longitude, latitude, and altitude. From Table 5, it can be seen that geographical factors are closely related to annual precipitation and extreme precipitation. In terms of latitude, CDD is positively correlated with latitude, while the other indices are negatively correlated with latitude. While the correlation between CWD and latitude is weak, with a coefficient value of −0.097, the remaining precipitation indices exhibit noteworthy correlations with latitude. The findings suggest that, in the SRB, areas with lower latitudes tend to have more precipitation overall, more frequent and intense extreme precipitation events, and fewer days of consecutive drought.
From the perspective of longitude, its effect on precipitation is significant. The correlation coefficient between R25mm and longitude has passed the significance test at the 0.05 level, while the remaining indices have all been found to be statistically significant at the 0.01 level. Except for CDD, the other precipitation indices are positively correlated with longitude. These results indicate that in the SRB, areas with higher longitudes tend to have more precipitation overall, more frequent and intense extreme precipitation events, and fewer consecutive dry days, with more consecutive wet days.
From the perspective of altitude, aside from CDD, all other indices show a positive correlation with altitude. Specifically, the correlation coefficient between altitude and CWD has passed the significance test at 0.01 level, while the correlation coefficients between altitude and annual precipitation, R10mm, and R20mm have passed the significance test at 0.05 level. From the results, it is evident that altitude plays a certain role in the frequency and persistence of extreme precipitation.

3.3.2. Local Air Temperature

The air temperature within the SRB is gradually rising. During the period 1968–2019, the annual average temperature in the basin rose by 0.37 °C/10a; the annual average maximum temperature increased by 0.25 °C/10a; and the annual average minimum temperature rose by 0.50 °C/10a.
The correlation between air temperature and indices for precipitation within the SRB is shown in Table 6. The annual average temperature and annual average minimum temperature are negatively correlated with annual precipitation, R10mm, R20mm, R25mm, and CWD, and positively correlated with the other precipitation indices. Meanwhile, the annual average maximum temperature is negatively correlated with annual precipitation, R10mm, R20mm, CWD, and R95p, and positively correlated with the others. Local air temperature is primarily significantly correlated with CWD, and the local annual average minimum temperature also has some influence on R99p. The results indicate that with the local air temperature rising, the persistent precipitation events weaken, and the intensity for precipitation increases to a certain degree.

3.3.3. Circulation Factors

The interplay of circulation factors and precipitation is depicted in Figure 9, highlighting the correlation effect. It can be seen that NAO, SOI, AO, and WP are negatively correlated with precipitation indices; PDO, Niño3, and Niño3.4 are positively correlated with precipitation indices; and Niño1+2 and Niño4, except for being negatively correlated with R99p and CDD respectively, are positively correlated with the remaining precipitation indices. The circulation factors mentioned above have relatively singular effects on precipitation indices in the SRB while almost the entire basin precipitation indices are significantly affected by the WPSH. The WPSHA and WPSHI have a non-significant negative correlation with CDD, a positive correlation at the 0.05 significance level with CWD and R99p, and a positive correlation at the 0.01 significance level with annual precipitation, R10mm, R20mm, R25mm, and R95p. Among these circulation factors, the extreme precipitation index CDD only has a significantly negative correlation with WP, passing the significance test at the 0.05 level. There are numerous circulation factors related to the extreme precipitation index CWD: in addition to the WPSHA and WPSHI, there are also significant positive correlations with Niño1+2, Niño3, and significant negative correlations with NAO.
Some of the selected circulation factors have a significant influence on the annual precipitation patterns in the SRB and drive the changes in precipitation patterns. The correlation coefficient between the time coefficient of EOF1(PC1) and WPSHA and WPSHI is above 0.4, passing the significance test at the 0.01 level, showing a significant positive correlation. There is also a significant correlation between PC1 and Niño4, with a correlation coefficient of 0.282, passing the significance test at the 0.05 level. The correlation coefficient between the time coefficient of EOF2(PC2) and Niño1+2 is −0.277, passing the significance test at the 0.05 level, indicating a significant negative correlation. From this, it can be seen that when WPSHA and WPSHI increase, the PC1 increases and the positive phase of the first mode becomes more pronounced. Similarly, when the Niño4 index strengthens, the positive phase of the first mode also becomes more pronounced. For EOF2, when the Niño1+2 index strengthens, the negative phase of the second mode becomes more pronounced.
The results from Pearson correlation analysis suggest that the WPSH exerts a noteworthy influence on precipitation and extreme precipitation in the SRB during the period 1968–2019. To better comprehend the relationship between precipitation and WPSH, this paper employs the cross-wavelet transform (XWT) and wavelet coherence analysis (WTC). The XWT and WTC can be used to analyze the common wavelet spectrum signals between annual precipitation and indices for extreme precipitation and circulation factors in order to determine the correlated characteristics. In Figure 10 and Figure 11, the region surrounded by the conical thin arcs is the valid wavelet spectra value, while the remaining regions denote invalid wavelet spectra values. The region surrounded by the thick line represents the region that passes the red noise standard spectrum test at the 0.05 significance level. The direction of the arrows indicates phase relationships. A vertical upward arrow indicates that the precipitation index lags behind the circulation factors by 1/4 period, while a vertical downward arrow indicates that the precipitation index leads the circulation factors by 1/4 period [60].
Figure 10 shows the periodic relationship by using XWT between precipitation and the WPSHA index and WPSHI index. Except for CWD, the significant resonance regions between WPSHA and WPSHI and other precipitation indices are similar in the high-energy spectrum region. As shown in Figure 10(a1), the WPSHA and annual precipitation have a significant positive correlation in 1980–1985 and 1993–2002, which is consistent with the results obtained by the Pearson correlation analysis. There are three significant resonance regions between them, including a period of 0.8–3.2a for 1980–1985, a period of 0.16–1.12a for 1993–1997, and a period of 2.88–5.12a for 1995–2002. The arrows point upwards for the significant regions of 1980–1985 and 1993–1997, indicating the changes in annual precipitation lag behind the WPSHA. The findings suggest that the WPSHA has a powerful influence on annual precipitation during the period 1993–2002 and exhibits multi-timescale characteristics. There are three significant resonance regions between CWD and WPSHA and WPSHI, namely a period of 0.32–2.72a for 1980–1985, a period of 0.32–5.12a for 1995–2003, and a period of 2.4–3.68a for 2008–2012. The first two periodic regions show rightward-pointing arrows, suggesting a positive correlation, whereas the third periodic region has leftward-pointing arrows, suggesting a negative correlation. The arrows of three significant regions are all downward. CWD changes in the time series lead ahead of the WPSH in terms of timing.
The PC1 has a significant resonance relationship with the WPSH in the high-energy zone, as shown in Figure 10(b1,b2). There are three significant resonance regions: a period of 1.9–3.8a for 1980–1985; a period of 3.2–3.8a for 1987–1992; and a period of 3.1–4.8a for 1994–2001. Within these significant resonance regions, the rightward arrows suggest a positive relationship. Additionally, during the period of 1980–1985, the arrows point upwards, suggesting that the changes of PC1 lag behind the WPSH in terms of timing.
At the local scale, except for CWD and R99p, the significant resonance region between the area and intensity of the WPSH and precipitation indices are mostly concentrated in 1980–1985, 1998–2001, and 2009–2015. During the first two regions, the arrows point to the upper right, indicating a positive correlation, and that the changes in precipitation lag behind the WPSH. In the third region, the arrows point to the lower right, indicating the changes in precipitation lead ahead of the WPSH. As shown in Figure 11(h1,h2), the significant resonance periods between the WPSH and R99p do not include the period 1980–1985. There are significant resonance regions between the CWD and the WPSH, but they are noticeably different. The significant resonance regions between them are: a period of 0–2.1a for 1980–1990; a period of 0–1.2a for 1993–1998; and a period of 2.3–5.2a for 1996–2004.
On the basis of WTC analysis, from Figure 11(b1), it can be observed that PC1 and WPSHA have three significant resonance regions, including a period of 0–2a for 1980–1985, a period of 4–5.5a for 1997–2001, and a period of 0.2–4.5a for 2009–2015. The direction is to the right, indicating a positive correlation. In the first two significant resonance regions, the arrows are biased upwards, indicating that the changes of PC1 lag behind the WPSHA. In the third resonance region, the arrow points downward, indicating a leading change. From Figure 11(b2), it can be seen that PC1 and WPSHI have significant resonance periods of 1.8–3.3a, 0–1.3a, and 0.1–4.2a during 1978–1982, 1980–1985, and 2010–2015, respectively.

4. Discussion

The study focused on analyzing the spatiotemporal variations of precipitation within the SRB throughout 52 years, which were the years from 1968 to 2019. To assess precipitation, annual precipitation and seven indices for extreme precipitation were used for evaluation. Regarding the time scale, during the study period, the extreme precipitation index CDD in the SRB exhibited a decreasing trend, while the other indices showed an increasing trend. The results align with those studied by Yu et al. [40]. The variations of precipitation exhibited a relatively obvious alternation between increase and decrease. For the mutation analysis, the cumulative anomaly method and Pettitt test were used in this paper. They were used in the study of annual and extreme precipitation mutation points within the SRB, to improve the accuracy of the mutation test and more objectively identify the mutation time. Except for the absence of mutation points for CWD and R99p, the mutation of annual precipitation, extreme precipitation frequency indices, and continuous dry days occurred after 2000, while the mutation of the R95p occurred before 2000. Spatially, apart from CDD, the annual precipitation distribution resembles that of the indices for extreme precipitation, with more precipitation in the southeast. Most stations in the entire basin show an increasing tendency, while stations with a decreasing tendency are primarily concentrated in the southwest of the SRB. Through analysis, it has been determined that two main spatial distribution patterns are present in relation to annual precipitation within the SRB. These patterns are referred to the consistent pattern across the basin and the phase opposition pattern between the east and west. These two modes effectively explain the annual precipitation distribution characteristics in the SRB from 1968 to 2019.
Geographical factors have a significant impact on precipitation. Through analysis, it can be found that the precipitation in the SRB is significantly influenced by latitude, longitude, and altitude. In areas with higher longitude and lower latitude, there is more precipitation and stronger extreme precipitation. Comparatively, in high-altitude areas within the basin, periods of consecutive wet days are longer and extreme precipitation is more frequent. This indicates that the possibility of flood disasters in these areas is relatively significant. The results from the analysis match the information provided in Figure 5. With the increase in local air temperature, atmospheric water vapor content also rises, intensifying local water circulation and having a certain impact on precipitation [61]. The role of local air temperature on SRB is mainly reflected in CWD. With increasing air temperature, there is a tendency for fewer consecutive wet days within the SRB. In the selected circulation factors, the WPSH exerts a leading role in the annual precipitation and extreme precipitation within the SRB. The WPSH has a pronounced effect on the climate of East Asia. Research by Jiang et al. [62] indicated that when the WPSH strengthened, the precipitation in China increased. In this study, apart from CDD, the WPSHA index and WPSHI index show a significant positive correlation with annual precipitation and indices for extreme precipitation within the SRB. When the intensity and area of the WPSH increase, there is more precipitation, more intense and frequent extreme precipitation, and longer consecutive precipitation within the SRB. In addition, Li et al. [44] also conducted research on how atmospheric circulation factors affect the SRB. They found that the precipitation in the basin is positively linked to WPSHI and negatively linked to the WPSHA. Some results of Li et al. are different from those in this paper. Through analysis, different results could arise due to the selection of varying periods for the investigation.
This paper analyzed the spatiotemporal characteristics for annual precipitation and extreme precipitation within the SRB and discussed some influencing factors. The study in this paper is mainly based on an annual scale. In the future, a more detailed study of the SRB can be conducted at a seasonal or monthly scale. In addition, the circulation factors used in this study mainly include some atmospheric circulation factors and sea surface temperature indices. Other circulation factors, such as monsoon indices and the Northeast Cold Vortex, which may affect precipitation in the basin, were not analyzed. In a future study, these aspects can be added for further discussion.

5. Conclusions

The main conclusions of this study are as follows:
(1)
With the exception of CDD, the annual precipitation and other extreme precipitation within the SRB from 1968 to 2019 all showed an increasing trend. Among them, the annual precipitation and frequency indices of extreme precipitation (R20mm, R25mm) were statistically significant at 0.1, and the intensity indices of extreme precipitation (R95p, R99p) were statistically significant at 0.05. This showed that the extreme precipitation within the SRB had a tendency for increasing intensity and frequency during the study period. By combining the cumulative anomaly method and Pettitt test, the effective mutation points for annual precipitation and R10mm in the SRB were determined to be in 2011; for R20mm and R25mm, the effective mutation points were in 2009; for CDD, the effective mutation point was in 2003; for R95p, the effective mutation point was in 1984; the effective mutation points for CWD and R99p were not determined.
(2)
In terms of spatial distribution, except for CDD, which showed a “more in the west, less in the east” pattern, annual precipitation and other extreme precipitation indicators exhibited a “more in the east, less in the west” pattern. The high-value zones were centralized in the southeast of the SRB, while zones with lower values were primarily centralized in the southwest.
(3)
Through Empirical Orthogonal Function decomposition, the annual precipitation can be divided into 2 modes. The first mode represents a consistent change pattern across the entire basin, showing either overall more or less precipitation. The second mode represents an “east-west” anti-phase pattern, with the eastern part experiencing more precipitation and the western part experiencing less precipitation, or vice versa.
(4)
Annual precipitation, frequency indices for extreme precipitation (R10mm, R20mm, R20mm), intensity indices for extreme precipitation (R95mm, R99mm), and CWD are negatively correlated with latitude, and positively correlated with longitude and altitude. The impact of geographical factors on the CDD is exactly the opposite. In the SRB, areas with low latitude and high longitude receive more precipitation, and have more frequent and intense events of extreme precipitation. The periods of consecutive wet days are longer in high-altitude areas. Local temperatures have a significant negative correlation with CWD. As temperature rises, the duration of precipitation events decreases. The annual precipitation and extreme precipitation are impacted by complex circulation factors. Among them, the impact of the WPSH on annual precipitation and extreme precipitation within the SRB from 1968 to 2019 is the most significant. Except for CDD, it is a notably positive linkage with other indices for precipitation and the first mode of annual precipitation. With the strengthening of the WPSHA and WPSHI, the precipitation in the SRB tends to be more abundant and intense. Wavelet coherence analysis and cross-wavelet transform also show that there are resonant periods of different time scales between them, further highlighting the important dominant role of the WPSH in the precipitation changes in the SRB.

Author Contributions

Conceptualization, Z.L. and H.Y.; methodology, H.Y.; software, H.Y.; validation, Z.L. and H.Y.; formal analysis, H.Y. and M.J.; resources, Z.L. and H.Y.; data curation, H.Y. and M.J.; writing—original draft preparation, H.Y.; writing—review and editing, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Basic Research Expenses of Provincial Colleges and Universities of Heilongjiang Province (Project No.: 2022-KYYWF-1238).

Data Availability Statement

The data used in this study can be found on the website mentioned in the article.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Geographic location and distribution of meteorological stations in the SRB.
Figure 1. Geographic location and distribution of meteorological stations in the SRB.
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Figure 2. Tendency variation of (a) annual precipitation and mean extreme precipitation indices: (b) R10mm, (c) R20mm, (d) R25mm, (e) CWD, (f) CDD, (g) R95p, (h) R99p within SRB during the period 1968–2019 (Z value means a statistical value in the Mann–Kendall trend test).
Figure 2. Tendency variation of (a) annual precipitation and mean extreme precipitation indices: (b) R10mm, (c) R20mm, (d) R25mm, (e) CWD, (f) CDD, (g) R95p, (h) R99p within SRB during the period 1968–2019 (Z value means a statistical value in the Mann–Kendall trend test).
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Figure 3. Anomaly and cumulative anomaly of (a) annual and extreme precipitation indices: (b) R10mm, (c) R20mm, (d) R25mm, (e) CWD, (f) CDD, (g) R95p, (h) R99p within SRB during the period 1968–2019.
Figure 3. Anomaly and cumulative anomaly of (a) annual and extreme precipitation indices: (b) R10mm, (c) R20mm, (d) R25mm, (e) CWD, (f) CDD, (g) R95p, (h) R99p within SRB during the period 1968–2019.
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Figure 4. Pettitt test on (a) annual precipitation and indices for extreme precipitation: (b) R10mm, (c) R20mm, (d) R25mm, (e) CWD, (f) CDD, (g) R95p, (h) R99p within SRB during the period 1968–2019.
Figure 4. Pettitt test on (a) annual precipitation and indices for extreme precipitation: (b) R10mm, (c) R20mm, (d) R25mm, (e) CWD, (f) CDD, (g) R95p, (h) R99p within SRB during the period 1968–2019.
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Figure 5. The spatial distribution for mean (a) annual precipitation and the seven extreme precipitation indices: (b) R10mm, (c) R20mm, (d) R25mm, (e) CWD, (f) CDD, (g) R95p, (h) R99p within the SRB during the period 1968–2019.
Figure 5. The spatial distribution for mean (a) annual precipitation and the seven extreme precipitation indices: (b) R10mm, (c) R20mm, (d) R25mm, (e) CWD, (f) CDD, (g) R95p, (h) R99p within the SRB during the period 1968–2019.
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Figure 6. Distribution of trend for (a) annual precipitation and the seven extreme precipitation indices: (b) R10mm, (c) R20mm, (d) R25mm, (e) CWD, (f) CDD, (g) R95p, (h) R99p within the SRB during the period 1968–2019.
Figure 6. Distribution of trend for (a) annual precipitation and the seven extreme precipitation indices: (b) R10mm, (c) R20mm, (d) R25mm, (e) CWD, (f) CDD, (g) R95p, (h) R99p within the SRB during the period 1968–2019.
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Figure 7. Eigenvalue distribution of the first two spatial modes: (a) mode 1, (b) mode 2 for annual precipitation within the SRB.
Figure 7. Eigenvalue distribution of the first two spatial modes: (a) mode 1, (b) mode 2 for annual precipitation within the SRB.
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Figure 8. Trend of time coefficients of the first two modes: (a) mode 1, (b) mode 2 for annual precipitation within the SRB.
Figure 8. Trend of time coefficients of the first two modes: (a) mode 1, (b) mode 2 for annual precipitation within the SRB.
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Figure 9. The correlation between circulation factors and annual and extreme precipitation. Note: ** and * have the same meaning as in Table 5.
Figure 9. The correlation between circulation factors and annual and extreme precipitation. Note: ** and * have the same meaning as in Table 5.
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Figure 10. The periodic relationships between WPSHA and WPSHI and precipitation: (a) annual precipitation, (b) PC1, (c) R10mm, (d) R20mm, (e) R25mm, (f) CWD, (g) R95p, (h) R99p by using XWT.
Figure 10. The periodic relationships between WPSHA and WPSHI and precipitation: (a) annual precipitation, (b) PC1, (c) R10mm, (d) R20mm, (e) R25mm, (f) CWD, (g) R95p, (h) R99p by using XWT.
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Figure 11. The periodic relationships between WPSHA and WPSHI and precipitation: (a) annual precipitation, (b) PC1, (c) R10mm, (d) R20mm, (e) R25mm, (f) CWD, (g) R95p, (h) R99p by using WTC.
Figure 11. The periodic relationships between WPSHA and WPSHI and precipitation: (a) annual precipitation, (b) PC1, (c) R10mm, (d) R20mm, (e) R25mm, (f) CWD, (g) R95p, (h) R99p by using WTC.
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Table 1. Description of indices for extreme precipitation.
Table 1. Description of indices for extreme precipitation.
ScaleExtreme Precipitation IndicesNameDefinitionUnit
Frequency indicesR10mmHeavy precipitation daysTotal annual days when daily precipitation > 10 mmdays
R20mmVery heavy precipitation daysTotal annual days when daily precipitation > 20 mmdays
R25mmExtremely heavy precipitation daysTotal annual days when daily precipitation > 25 mmdays
Persistence indicesCWDConsecutive wet daysMaximum number of consecutive days when daily precipitation ≥ 1 mmdays
CDDConsecutive dry daysMaximum number of consecutive days when daily precipitation < 1 mmdays
Intensity indicesR95pPrecipitation on wet daysTotal annual precipitation when daily precipitation > 95th percentilemm
R99pPrecipitation on very wet daysTotal annual precipitation when daily precipitation > 99th percentilemm
Table 2. Contrast between cumulative anomaly method and Pettitt test.
Table 2. Contrast between cumulative anomaly method and Pettitt test.
Precipitation IndicesCumulative Anomaly MethodPettitt TestThe Final Point of Mutation
Annual precipitation1982, 1998, 201120112011
R10mm1982, 1998, 201120112011
R20mm1982, 1998, 200920092009
R25mm1982, 1998, 200920092009
CWD1979, 1988, 1995, 1998, 20081979 (αt > 0.5)—
CDD1984, 1990, 200320032003
R95p1984, 1998, 20111982,19841984
R99p1984, 1998, 20111983—
Table 3. The first six eigenvectors of decomposition of annual precipitation within SRB during the period 1968–2019.
Table 3. The first six eigenvectors of decomposition of annual precipitation within SRB during the period 1968–2019.
ModeEigenvalueVariance Contribution Rate (%)Cumulative Variance Contribution Rate (%)The Upper Limit of Eigenvalue ErrorThe Lower Limit of Eigenvalue Error
1391,908.9143.19%43.19%320,356.46463,461.37
2101,415.3411.17%54.36%818,99.52119,931.17
379,609.068.77%63.13%65,074.5094,143.61
442,225.364.66%67.79%34,516.1049,934.62
527,438.883.02%70.81%22,429.2532,448.52
624,848.812.74%73.55%20,312.0629,385.56
Table 4. Typical years for modes 1 and 2 of annual precipitation in SRB during the period 1980–2019.
Table 4. Typical years for modes 1 and 2 of annual precipitation in SRB during the period 1980–2019.
ModeTime CoefficientsPrecipitation CharacteristicsTypical Years
1Positive valueMore precipitation in the whole basin1969, 1981, 1983, 1984, 1985, 1987, 1990, 1991, 1994, 1998, 2005, 2012, 2013, 2016, 2018, 2019
Negative valueLess precipitation in the whole basin1968, 1970, 1972, 1975, 1976, 1978, 1979, 1982, 1989, 1992, 1996, 1997, 1999, 2000, 2001, 2004, 2006, 2007, 2008, 2011
2Positive valueLess precipitation in the west and more precipitation in the east1971, 1973, 1974, 1980, 1986, 1995, 2002, 2010, 2017,
Negative valueMore precipitation in the west and less precipitation in the east1977, 1988, 1993, 2003, 2009, 2014, 2015
Table 5. The correlation between geographical factors and annual and extreme precipitation.
Table 5. The correlation between geographical factors and annual and extreme precipitation.
Precipitation IndicesLatitudeLongitudeAltitude
Annual precipitation−0.422 **0.560 **0.307 *
R10mm−0.415 **0.547 **0.305 *
R20mm−0.328 *0.332 **0.313 *
R25mm−0.343 **0.271 *0.250
CWD−0.0970.419 **0.558 **
CDD0.422 **−0.715 **−0.182
R95p−0.450 **0.542 **0.236
R99p−0.410 **0.557 **0.116
Note: ** indicates a significant correlation at the 0.01 level; * indicates a significant correlation at the 0.05 level.
Table 6. The correlation between temperature factors and annual and extreme precipitation.
Table 6. The correlation between temperature factors and annual and extreme precipitation.
Precipitation IndicesAnnual Average TemperatureAnnual Average Maximum TemperatureAnnual Average Minimum Temperature
Annual precipitation−0.094−0.103−0.070
R10mm−0.110−0.116−0.086
R20mm−0.097−0.077−0.089
R25mm−0.0210.008−0.021
CWD−0.452 **−0.399 **−0.431 **
CDD0.0640.1130.017
R95p0.100−0.0590.244
R99p0.1820.0500.288 *
Note: ** and * have the same meaning as in Table 5.
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Li, Z.; Yang, H.; Jia, M. Factors Affecting the Spatiotemporal Variation of Precipitation in the Songhua River Basin of China. Water 2024, 16, 2. https://doi.org/10.3390/w16010002

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Li Z, Yang H, Jia M. Factors Affecting the Spatiotemporal Variation of Precipitation in the Songhua River Basin of China. Water. 2024; 16(1):2. https://doi.org/10.3390/w16010002

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Li, Zhijun, Hongnan Yang, and Minghui Jia. 2024. "Factors Affecting the Spatiotemporal Variation of Precipitation in the Songhua River Basin of China" Water 16, no. 1: 2. https://doi.org/10.3390/w16010002

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