Impacts of Extreme Climate on the Water Resource System in Sichuan Province

Abstract

Based on the data of Sichuan Province from 2007 to 2021, the extreme climate events in Sichuan Province was identified by statistical method, and the coupling coordination degree of water resources-climate system and separate water resource system was analyzed. Based on the difference of coupling coordination degree under these two systems, the influence mechanism of extreme climate factors on the water resource system is analyzed. The results show that the extreme climate types in Sichuan Province gradually transition from extreme drought to extreme precipitation and from extreme low temperature to extreme high temperature. When climate factors are not considered, the coupling coordination degree of the water resource system is generally improved and the distribution is more concentrated. Moreover, the influence of climate factors on the water resource system is not a simple linear relationship.

1. Introduction

In the Sixth Assessment Report of the IPCC [1], “extreme weather events in climate change” has been added, which indicates that extreme climate is happening more and more frequently, and researchers have paid more attention to this issue. Many scholars have analyzed the temporal and spatial changes of extreme climate events [2,3,4] and the effects of extreme climate in a certain region [5,6,7]. Extreme precipitation has a huge impact on agricultural production. Wang et al. [8] analyzed the changes of extreme precipitation in South China by using the machine learning method and concluded that extreme precipitation in South China in recent years decreased from April to May and increased from June to August. It is mainly affected by the ECPC (extreme precipitation circulation pattern), dynamic, and thermodynamic. Li et al. [9] used trend analysis to analyze the global extreme precipitation events and concluded that the proportion of extreme precipitation was on the rise worldwide. Zhu et al. [10] found through research that extreme precipitation has an accelerated effect on nitrate leaching in farmland. Extremely high temperatures pose a great threat to the survival and development of global organisms. Gao et al. [11] believe that after an extremely high temperature occurs, the abundance of the large-animal community in agricultural soil decreases. Yadav et al. [12] believe that high-temperature and heat wave events increase mortality and urban heat island effect. Stoks et al. [13] believe that an extremely high temperature has a great impact on all biological interactions (such as predation, competition, parasitism, etc.). Moreover, many studies have shown that extreme precipitation and high temperature interact [14,15,16], and that the occurrence of heat wave events shortens the time interval of extreme precipitation [17,18,19,20,21]. Extreme drought mainly has a great impact on cultivated land systems and water ecological environments. Under the effect of extreme drought events, ecological functions of crops are inhibited, photosynthetic efficiency and productivity are destroyed [22], and extreme drought also accelerates the discharge of groundwater in floodplain [23]. The simultaneous effect of extremely low temperatures and reduced water content inhibit the respiration of the alpine steppe on the Tibetan Plateau [24]. During the slowdown of global warming, extreme low winter temperature events in Eurasia have a recovery trend [25].

Water resources are indispensable natural resources in the process of social development. The research on water resource systems mainly focuses on two aspects: sustainability [26,27] and carrying capacity [28]. The two major ways of water input and output in the water resource system are precipitation and evaporation, which are mainly affected by climate factors, so climate factors interact with the water resource system [29]. Moses et al. [30] analyzed the effects of wind speed, solar radiation, and relative humidity on the water resources of Okavango Delta through the K-M test method, and the results showed that with the continuous development of climate, the evaporation of water resources in this region would be further increased. Clifton et al. [31] use a model to evaluate the impact of snow cover on the water resources of Blue Mountains in Oregon, USA, and find that in areas with less groundwater resources, the reduction in snow cover and summer rainfall will lead to the reduction in the water supply of aquatic ecosystems and agriculture. Anik et al. [32] studied a large number of references and concluded that Bangladesh faced the challenge of water scarcity due to climate change. Globally, the frequency of extreme climate events is gradually increasing. Wang et al. [33] believe that the global farmland will be exposed to complex drought and heat wave events in the future, which will lead to a continuous increase in agricultural water use in the future. Yang et al. [34] believe that climate change is the main factor leading to population exposure, and it is expected that the population exposure in Asia will be the largest in the future. In this case, it is crucial to ensure the safety of water supply. Therefore, from the global perspective, it is necessary to analyze the impact of extreme climate factors on the water resource system. Since the occurrence and distribution of extreme climate events will vary greatly due to the different topography and geomorphology in the study area, the selection of a small research scope can describe the impact mechanism of extreme climate on the water resource system more accurately.

Based on the above analysis, this paper will analyze the extreme climate events in Sichuan Province from 2007 to 2021, explore the coupling coordination degree of the water resource system when extreme climate events occur, and compare the coupling coordination degree of the water resource–climate resource system and a separate water resource system from 2015 to 2021. The difference between the two is used as an indicator of the degree of influence of extreme climate factors on water resources, aiming to reveal the mechanism of action of climate factors on the water resource system under extreme climate events, and provide a theoretical basis for Sichuan Province to cope with extreme climate events.

2. Materials and Methodology

2.1. Materials

2.1.1. Study Area

With a total area of 48.6 km² and a jurisdiction over 21 prefectures, Sichuan Province lies between 26°03′~34°19′ north latitude and 97°21′~108°12′ east longitude (Figure 1). The topography of Sichuan Province varies greatly, with a high elevation in the east and a low elevation in the west. It is composed of mountains, hills, plains, basins, and plateaus. The main rivers in Sichuan Province are the Yellow River, the Yangtze River, Yalong River, Jinsha River, Minjiang River, Dadu River, and so on.

2.1.2. The Index System

The evaluation is mainly constructed by the climate system and water resource system, in which the water resource system is divided into the water quantity subsystem, water supply subsystem, and water quality subsystem. After studying the previous references [35,36,37,38,39,40,41] and testing the data consistency, 3 indicators were selected for each subsystem, and a total of 12 indicators were selected to calculate the coupling coordination degree. The specific selected indicators are shown in

Table 1. Sichuan Province water resource–climate system evaluation indicator system.

System Layer	Element Layer	Indicator Layer	Unit	Nature
Water resource–climate system	water quantity subsystem	surface water resources	108 m3	+
		underground water resources	108 m3	+
		total water resources	108 m3	+
	water supply subsystem	surface water supply	108 m3	+
		groundwater supply	108 m3	+
		water supply from other water resources	108 m3	+
	water quality subsystem	sewage discharge	104 m3	−
		sewage treatment rate	%	+
		precipitation pH	-	+
	climate subsystem	rainfall	mm	−
		temperature	°C	−
		relative humidity	%	−

.

It is worth noting that in previous studies, scholars included rainfall in other subsystems. For example, Xu et al. [42] included it in the water subsystem, Tao et al. [43] included it in the water resource situation subsystem, and Xu et al. [44] included it in the ecological subsystem. However, rainfall was included in the climate subsystem in this paper.

2.1.3. Sources of Data

The data selected for this paper are mainly from the Sichuan Statistical Yearbook [45], Sichuan Meteorological Bulletin [46], Sichuan Water Resources Bulletin [47], and Sichuan Environmental Bulletin [48] for the years 2007–2021, as well as the Water Resources Bulletin and the Statistical Yearbook for the 21 districts in the province.

2.2. Methodology

2.2.1. Statistical Method

For the identification of extreme climate events, many scholars have adopted statistical methods. For example, Kuwanto et al. [49] used the extreme value theory to analyze the extreme precipitation events in Indramayo, Indonesia. Zhao et al. [50] used the non-parametric M-K test method to calculate urban climate extremes in China; Stocker et al. [51] defined climatic events with a probability of less than 10% as extreme climate events. In this paper, the anomaly and standard deviation are used to analyze extreme climate events. The specific calculation formula is as follows:

$$S=\sqrt{\frac{1}{n-1}{\displaystyle {\sum }_{i=1}^n{(\overline{x}-x_i)}^2}},$$

(1)

$$D=x_i-\overline{x},$$

(2)

$$\gamma =\frac{D}{S},$$

(3)

In the formula, $S$ represents the standard deviation, $D$ represents the anomaly, $x_i$ represents the value of rainfall or temperature in the year i of a region, and $\overline{x}$ represents the multi-year average of rainfall or temperature in a region. When $\gamma \ge 1.2$, it is defined that extreme precipitation or extreme high temperature events have occurred in the region. When $\gamma \le -1.2$, it is defined that an extreme drought or extreme low temperature event occurred in the area.

In the part of exploring the influence mechanism of extreme climate on the water resource system, in order to increase the amount of data and obtain a more accurate fitting function, the judgment conditions of extreme climate are appropriately relaxed in this part. When $\gamma \ge 1.1$, it is defined that extreme precipitation or extreme high temperature events have occurred in the region. When $\gamma \le -1.1$, it is defined that an extreme drought or extreme low temperature event occurred in the area.

2.2.2. Calculation of the Coupling Coordination Degree

Standardization of Data

The dimensions of the data collected in this paper are different, so it is necessary to standardize the data, and the standardized values were made to lie between 0.001 and 0.999, using the following formula [52]:

Positive indicator:

$$x_i^{\prime }=0.001+\frac{(x_i-x_{\min })(0.999-0.001)}{x_{\max }-x_{\min }},$$

(4)

Negative indicator:

$$x_i^{\prime }=0.001+\frac{(x_{\max }-x_i)(0.999-0.001)}{x_{\max }-x_{\min }},$$

(5)

In the formula, $x_i^{\prime }$ is the value of the indicator after normalization, $x_i$ is the true value of the indicator, and $x_{\max }$ and $x_{\min }$ are the maximum and minimum values of the indicator, respectively.

Determination of Weights

• Calculation of subjective weights on IFAHP

The intuitionistic fuzzy analytic hierarchy process (IFAHP) [53] introduces the intuitionistic fuzzy set in the basic hierarchical analysis method, which can quantify the measured values of the indicators by describing the scores of the program with the affiliation function. In order to solve the ambiguities occurring during the evaluation process and minimize the influence of human factors, it is necessary to improve the reasonableness of the subjective weighting, which is calculated as follows [54]:

(1) Construct the comparison matrix $R$

It is assumed that the number of experts attended the decision-making process is $K$. Each element $r_{ij}^k$ in the comparison matrix $R$ of the two indicators is an intuitionistic fuzzy number corresponding to the evaluation result.

$$R^k=\left(\begin{array}{cccc}1& r_{12}^k& \cdots & r_{1n}^k\\ r_{21}^k& 1& \cdots & r_{2n}^k\\ ⋮& ⋮& \ddots & ⋮\\ r_{n1}^k& r_{n2}^k& \cdots & 1\end{array}\right), k=1,2,\cdots ,K,$$

(6)

where $r_{ij}=\left({\mu }_{ij},{\nu }_{ij}\right)$, ${\mu }_{ij}$ denotes the degree of affiliation, which is the level of preference of the decision maker for indicator i, and ${\nu }_{ij}$ denotes the degree of non-affiliation, which is the level of preference for indicator j by the decision-maker. ${\lambda }_{ij}$ expression of the level of uncertainty of the decision maker, ${\lambda }_{ij}=1-{\mu }_{ij}-{\nu }_{ij}$.

(2) Construct the consistency intuitionistic fuzzy judgement matrix $\overline{R}={({\overline{r}}_{ij})}_{n\times n}$

• When $j>i+1$ or $j<i$, $r_{ij}=({\overline{\mu }}_{ij},{\overline{\nu }}_{ij})$.

Where

$${\overline{\mu }}_{ij}=\frac{\sqrt[j-i-1]{{\displaystyle \prod _{t=i+1}^{j-1}{\mu }_{it}{\mu }_{tj}}}}{\sqrt[j-i-1]{{\displaystyle \prod _{t=i+1}^{j-1}{\mu }_{it}{\mu }_{tj}}}+\sqrt[j-i-1]{{\displaystyle \prod _{t=i+1}^{j-1}(1-{\mu }_{it})(1-{\mu }_{tj})}}},$$

(7)

$${\overline{\nu }}_{ij}=\frac{\sqrt[j-i-1]{{\displaystyle \prod _{t=i+1}^{j-1}{\nu }_{it}{\nu }_{tj}}}}{\sqrt[j-i-1]{{\displaystyle \prod _{t=i+1}^{j-1}{\nu }_{it}{\nu }_{tj}}}+\sqrt[j-i-1]{{\displaystyle \prod _{t=i+1}^{j-1}(1-{\nu }_{it})(1-{\nu }_{tj})}}},$$

(8)

• When $j=i+1$ or $j=i$, ${\overline{r}}_{ij}=r_{ij}$.

(3) Determine the distance between $\overline{R}$ and $R$

$$d(\overline{R},R)=\frac{{\displaystyle {\sum }_{i=1}^n{\displaystyle {\sum }_{j=1}^n\left(\left|{\overline{\mu }}_{ij}-{\mu }_{ij}\right|+\left|{\overline{\nu }}_{ij}-{\nu }_{ij}\right|+\left|{\overline{\lambda }}_{ij}-{\lambda }_{ij}\right|\right)}}}{2(n-1)(n-2)},$$

(9)

When $d(\overline{R},R)>\varsigma$ [55], the judgment matrix is considered to pass the consistency test. For intuitionistic fuzzy judgment matrices that do not satisfy the consistency test, a correction is required, and the corrected matrix is $R^{\prime }={(r_{ij}^{\prime })}_{n\times n}$.The correction method is as follows:

$${\mu }_{ij}^{\prime }=\frac{{\mu }_{ij}^{1-\sigma }{\overline{\mu }}_{ij}^{\sigma }}{{\mu }_{ij}^{1-\sigma }{\overline{\mu }}_{ij}^{\sigma }+{(1+{\mu }_{ij})}^{1-\sigma }(1-{\overline{\mu }}_{ij})\sigma },$$

(10)

$${\nu }_{ij}^{\prime }=\frac{{\nu }_{ij}^{1-\sigma }{\overline{\nu }}_{ij}^{\sigma }}{{\nu }_{ij}^{1-\sigma }{\overline{\nu }}_{ij}^{\sigma }+{(1+{\nu }_{ij})}^{1-\sigma }{(1-{\overline{\nu }}_{ij})}^{\sigma }},$$

(11)

(4) Calculation of subjective weights

The calculation of indicator weights for judgement matrices that satisfy consistency is performed with the following formula:

$$W_i^{\prime }=\frac{{\mu }_i+\lambda \left(\frac{{\mu }_i}{{\mu }_i+{\nu }_i}\right)}{{\displaystyle {\sum }_{j=1}^n\left[{\mu }_i+{\lambda }_i+\left(\frac{{\mu }_i}{{\mu }_i+{\nu }_i}\right)\right]}},$$

(12)

In the formula, $W_i^{\prime }$ is the subjective weight.

2.

Calculation of objective weights based on the improved CRITIC method

The CRITIC method was proposed by Diakoulaki et al. [56] and applied to the calculation of objective weighting, which can reflect the strength and conflict of the indicators well. However, it lacks the consideration of data dispersion, so discrete coefficients should be introduced into the calculation in order to find more reasonable objective weights [57], which are calculated as follows:

$$W_i^{″}=\frac{({\sigma }_i+{\delta }_i){\displaystyle {\sum }_{t=1}^n(1-r_{ij})}}{{\displaystyle {\sum }_{t=1}^n({\sigma }_i+{\delta }_i){\displaystyle {\sum }_{t=1}^n(1-r_{ij})}}},$$

(13)

where ${\sigma }_i$ is the mean difference of indicator $i$, ${\delta }_i$ is the newly introduced discrete coefficient, $r_{ij}$ is the correlation coefficient between indicator $i$ and $j$, and $W_i^{″}$ is the objective weight.

3.

Calculation of composite weights

In this paper, the subjective and objective weights are considered together and considered equally important, so the coefficient $\alpha =\beta =0.5$ is made to be calculated as follows:

$$W_i=\alpha W_i^{\prime }+\beta W_i^{″},$$

(14)

where $W_i$ is the composite weights.

Modeling of the Coupling Coordination Degree

$$C=4\times {\left\{\frac{f(a)\times g(b)\times h(c)\times q(d)}{{\left[f(a_t)+g(b_t)+h(c_t)+q(d_t)\right]}^4}\right\}}^{\frac{1}{4}},$$

(15)

where $C$ is the degree of coupling; $f(a)$, $g(b)$, $h(c)$, $q(d)$ are the development indexes of the water quantity subsystem, the water supply subsystem, the water quality subsystem, and the climate subsystem; $f(a_t)$, $g(b_t)$, $h(c_t)$, $q(d_t)$ are the development indexes of the four subsystems in year $t$.

$$T=w_{\alpha }f(a_t)+w_{\beta }g(b_t)+w_{\gamma }h(c_t)+w_{\tau }q(d_t),$$

(16)

where $T$ is the development index of the water resource–climate system, and $w_{\alpha }$, $w_{\beta }$, $w_{\gamma }$, $w_{\tau }$ are the weights of the four subsystems.

$$D=\sqrt{CT},$$

(17)

where $D$ is the coupling coordinate degree of the water resource–climate system.

The evaluation grades of coupling coordinate degree are shown in

Table 2. Classification of the coupling coordination degree levels.

Range of Coupling Coordination Degree	Serial Number	Coordination Level
[0.0, 0.1)	1	extreme imbalance
[0.1, 0.2)	2	serious imbalance
[0.2, 0.3)	3	moderate imbalance
[0.3, 0.4)	4	mild imbalance
[0.4, 0.5)	5	imminent imbalance
[0.5, 0.6)	6	barely coordination
[0.6, 0.7)	7	primary coordination
[0.7, 0.8)	8	intermediate coordination
[0.8, 0.9)	9	good coordination
[0.9, 1.0]	10	quality coordination

, and the calculated coupling coordinate degree is divided into 10 grades [58], in which the former five grades belong to the state of system imbalance, and the latter five grades belong to the state of system coordination.

3. Results

3.1. Distributional Characteristics of Extreme Climate Events

3.1.1. Characteristics of the Temporal Distribution of Extreme Climate Events

According to the above statistical methods, extreme climate events in Sichuan Province in the past 15 years were analyzed, and the results are shown in Figure 2. It can be seen from the figure that the frequency of extreme climate events shows an overall upward trend, which is consistent with the environment in which extreme climate events have become more frequent in many regions in recent years [59,60,61]. In the past 15 years, three cities had extreme drought events in 2009, which was the year with the fewest extreme climate events. In 2015, nine cities had extreme high temperature events, two cities had extreme precipitation events, and one city had extreme drought events, which was the year with the most extreme climate events. In order of frequency, the five extreme events were: extreme precipitation events (34%) > extreme drought events (24%) > extreme high temperature events (18%) = extreme low temperature events (18%) > comprehensive extreme climate events (6%).

The frequency of extreme precipitation events is the highest, but as can be seen from Figure 2, the frequency of extreme precipitation events was low before 2014, and extreme precipitation events began to occur frequently in 2015. The extreme drought events all occurred between 2007 and 2018, and there was no such extreme climate in 2019–2021. Although the frequency of extreme low temperature events and extreme high temperature events is equal, the extreme low temperature events mostly occurred between 2007 and 2014 (79% of the total number of extreme low temperature events occurred during the study period), and the extreme high temperature events mostly occurred after 2014 (60% of the total number of extreme high temperature events occurred during the study period). Through the above analysis, it can be concluded that the type of extreme climate occurring in Sichuan Province gradually transitions from extreme drought to extreme precipitation, and from extreme low temperature to extreme high temperature. These conclusions are consistent with those of Wang et al. [62], Rush et al. [63], and Zhu et al. [64].

The statistical data of specific extreme climate events are shown in

Table 3. The maximum, minimum, and average values of rainfall and temperature in 21 regions of Sichuan Province.

City	Rainfall (mm)			Temperature (°C)
	Maximum	Minimum	Average	Maximum	Minimum	Average
Aba Tibetan and Qiang Autonomous Prefecture	1074.4	588.8	858.3	9.9	8.9	9.4
Bazhong	1728.7	863.1	1204.6	17.9	16.3	17.1
Chengdu	1343.3	610.9	972.2	16.9	15.9	16.5
Dazhou	1638.1	976.1	1271.4	18.6	17.3	17.9
Deyang	1650.6	711.0	963.2	17.9	16.0	16.9
Tibetan Autonomous Prefecture of Garzê	994.9	712.9	847.1	8.4	7.1	7.9
Guang’an	1538.6	837.6	1144.7	18.3	17.2	17.6
Guangyuan	1485.1	895.4	1036.2	17.3	17.1	16.5
Leshan	1555.3	768.9	1226.7	18.8	17.2	18.1
Yi Autonomous Prefecture of Liangshan	1284.3	558.2	1015.3	19.2	17.3	17.8
Luzhou	1443.7	765.0	1149.5	18.6	17.2	18.1
Meishan	1310.5	754.9	999.2	18.4	17.1	17.8
Mianyang	1367.0	545.5	911.7	18.1	16.5	17.3
Nanchong	1262.8	863.6	1095.4	18.9	17.4	17.9
Neijiang	1246.3	646.5	1000.9	18.6	17.1	17.9
Panzhihua	1053.4	537.7	759.7	22.6	20.5	21.3
Suining	1311.0	795.6	1024.5	18.2	16.8	17.6
Ya’an	2161.3	1833.6	1729.5	17.5	15.9	16.8
Yibin	1746.0	644.2	1078.8	19.3	17.5	18.4
Ziyang	1203.3	633.3	918.3	18.6	17.0	17.8
Zigong	1223.2	605.2	970.4	19.3	17.8	18.6

,

Table 4. Identification of extreme high temperature events.

Time	City	γ	Time	City	γ	Time	City	γ
2021	Panzhihua	1.24	2015	Suining	1.47	2015	Yibin	1.30
2013	Zigong	1.54		Leshan	1.29	2016	Dazhou	1.40
	Guangyuan	2.39		Nanchong	2.27		Bazhong	1.57
	Neijiang	1.63		Meishan	1.39	2019	Panzhihua	2.11
	Nanchong	2.27		Bazhong	1.38		Yi Autonomous Prefecture of Liangshan	2.69
	Yibin	1.46		Ziyang	1.70	2021	Aba Tibetan and Qiang Autonomous Prefecture	1.83
2015	Mianyang	1.42		Dazhou	1.26		Yi Autonomous Prefecture of Liangshan	1.38

,

Table 5. Identification of extreme precipitation events.

Time	City	γ	Time	City	γ	Time	City	γ
2007	Dazhou	1.25	2016	Yibin	2.30	2020	Meishan	1.78
2010	Ya’an	1.45	2017	Tibetan Autonomous Prefecture of Garzê	1.33		Leshan	1.32
2012	Yi Autonomous Prefecture of Liangshan	1.53		Guang’an	1.62		Yibin	2.30
	Neijiang	1.21	2018	Mianyang	2.10	2021	Mianyang	1.53
2013	Chengdu	1.75		Deyang	2.89		Guangyuan	2.38
	Zigong	1.41		Chengdu	1.31		Dazhou	1.65
	Luzhou	1.41		Ya’an	1.73		Guang’an	1.82
	Suining	2.01		Meishan	1.89		Nanchong	1.33
2014	Dazhou	1.37		Ziyang	1.74		Ya’an	1.42
	Yi Autonomous Prefecture of Liangshan	1.79	2019	Aba Tibetan and Qiang Autonomous Prefecture	1.59		Neijiang	1.27
2015	Panzhihua	2.22		Bazhong	2.05		Zigong	1.51
	Tibetan Autonomous Prefecture of Garzê	1.31		Ziyang	1.65		Bazhong	2.00
2016	Panzhihua	1.48	2020	Aba Tibetan and Qiang Autonomous Prefecture	1.34	/
	Luzhou	1.30		Chengdu	1.21

,

Table 6. Identification of extreme low temperature event.

Time	City	γ	Time	City	γ	Time	City	γ
2007	Panzhihua	−1.34	2010	Suining	−1.49	2012	Luzhou	−2.20
	Yi Autonomous Prefecture of Liangshan	−1.21		Meishan	−1.22	2013	Panzhihua	−1.51
2008	Zigong	−1.24	2011	Deyang	−1.76	2014	Chengdu	−1.28
	Neijiang	−1.29		Mianyang	−1.60		Ziyang	−1.78
	Dazhou	−1.35		Meishan	−1.46	2019	Guangyuan	−1.26
2010	Chengdu	−1.28		Ya’an	−1.34		Yibin	−1.90
	Zigong	−1.59	2012	Chengdu	−1.63		Bazhong	−1.37
	Mianyang	−1.22		Luzhou	−2.20	2021	Bazhong	−1.20

and

Table 7. Identification of extreme drought events.

Time	City	γ	Time	City	γ	Time	City	γ
2007	Chengdu	−1.46	2010	Luzhou	−1.56	2016	Mianyang	−1.69
	Leshan	−1.24		Deyang	−1.51		Guangyuan	−1.34
	Meishan	−1.48	2011	Zigong	−2.18		Nanchong	−1.47
	Ziyang	−1.74		Panzhihua	−1.68		Bazhong	−1.33
2008	Chengdu	−1.46		Neijiang	−1.83	2017	Leshan	−1.52
	Leshan	−1.24		Leshan	−1.84		Neijiang	−1.71
2009	Meishan	−1.48	2012	Chengdu	−1.70		Ziyang	−1.53
	Ziyang	−1.74		Guang’an	−1.99		Suining	−1.61
	Chengdu	−1.46		Guangyuan	−1.31	2018	Dazhou	−1.33
2010	Leshan	−1.24	2015	Ya’an	−1.36	/

. Due to the large amount of data, only the statistical data of extreme climate events are listed. It can be seen from Table 3 that in the past 15 years, the maximum and minimum rainfall values occurred in Ya’an and Panzhihua, respectively, and the highest and lowest temperature values occurred in Panzhihua and Tibetan Autonomous Prefecture of Garzê, respectively.

3.1.2. Characteristics of the Spatial Distribution of Extreme Climate Events

From the extreme climate identification in Table 4, Table 5, Table 6 and Table 7, it can be seen that Chengdu and Panzhihua experienced eight extreme climate events; Dazhou, Neijiang and Zigong experienced seven extreme climate events; Bazhong, Guangyuan, Meishan, Mianyang and Ziyang experienced six extreme climate events; Leshan, Liangshan Yi Autonomous Prefecture, Nanchong, Ya’an and Yibin experienced five extreme climate events; Aba Tibetan and Qiang Autonomous Prefecture, Guang’an, Luzhou and Suining have experienced four extreme climate events; and Tibetan Autonomous Prefecture of Garzê and Deyang have experienced three extreme climate events.

With the approval of the Sichuan government, administrative districts of some towns in Sichuan Province were adjusted in 2015. Therefore, when studying the spatial distribution characteristics of extreme climate events, the research interval was adjusted to 2015–2021. The boundary data of administrative divisions of Sichuan Province during 2015–2021 came from the Resources and Environmental Science Data Registration and Publication system [65]. The spatial characteristics of extreme climate events in the past seven years are analyzed below, and the specific distribution is shown in Figure 3.

As can be seen from Figure 3, extreme precipitation events basically occur in the whole region of Sichuan Province without obvious spatial distribution characteristics. Extreme drought events mostly occur in the northern and central regions of Sichuan Province, extreme high temperature events mostly occur in the western, central, and eastern regions of Sichuan province, and extreme low temperature events occur at a low frequency, only in Guangyuan, Bazhong and Suining. In the past seven years, three comprehensive extreme climate events have occurred, all of which occurred in Bazhong, namely, the comprehensive extreme climate of high temperature and drought in 2016, the comprehensive extreme climate of low temperature and precipitation in 2019 and 2021. The average annual temperature of Bazhong City in 2016 was 17.9 °C, 5% higher than the annual average temperature. The annual rainfall is 863.1 mm, 28% lower than the annual average rainfall, and the rainfall in 2019 and 2021 is 1728.7 mm and 1716.6 mm, respectively, 44% and 43% higher than the annual average rainfall.

3.2. Distributional Characteristics of Extreme Climate Events

According to the calculation method of coupling coordination degree mentioned above, the coupling coordination degree of the water resource–climate system in 21 districts of Sichuan Province from 2015 to 2021 was calculated, and the calculation results are shown in Figure 4.

In recent 7 years, the coupling coordination degree of Sichuan Province fluctuated between serious imbalance and good coordination, and the fluctuation range was large, but most of the coupling coordination levels were between mild imbalance and intermediate coordination. In 2016, the coupling coordination degree of Yibin was 0.13, which was in a serious imbalance state. The coupling coordination degree values of Leshan (2017), Neijiang (2017), Ziyang (2018) and Dazhou (2019) are 0.26, the coupling coordination degree values of Ya’an (2017) and Yibin (2019) are 0.27, the coupling coordination degree values of Aba Tibetan and Qiang Autonomous Prefecture (2019), Guangyuan (2019) and Ya’an (2021) are 0.21, 0.25 and 0.29, respectively, and the above 9 regions are in the moderate imbalance level. The coupling coordination degree values of Guang’an (2019), Ya’an (2020) and Nanchong (2017) are 0.81, 0.81, and 0.82, respectively, and these three cities are in good coordination level.

Combined with Figure 3, it is found that the coupling coordination levels of the regions where extreme climate events occur are all low, and most of the evaluation levels are between a moderate imbalance and barely coordinated. Among them, 13% of the regions fall under moderate imbalance, 34% under mild imbalance, 25% under imminent imbalance, and 15% under barely coordinated. Only Yibin had a serious imbalance level in 2016. Extreme precipitation weather occurred in this year. The γ of rainfall in this year was 2.3, which was a very extreme precipitation year, so the coupling coordination degree was at the lowest value. The evaluation levels of 11% of the regions are in primary coordination and intermediate coordination, among which Bazhong City in 2021 has a comprehensive extreme climate of precipitation and low temperature, the γ of rainfall in this year is 2.0, and the γ of temperature is −1.2. In the case that the two factors are not in line with the values of normal years, the coupling degree of the two extreme data is relatively high, thus it had a high evaluation grade. Therefore, when there are multiple attributes or multiple dimensional factors in an evaluation system, the evaluation of coupling coordination degree should be comprehensively considered. It is not that the higher the value of coupling coordination degree, the better the level of comprehensive development of a region.

3.3. Calculation of Coupling Coordination Degree of the Water Resource System

Without considering the influence of climate on the water resource system, the coupling coordination degree of a single water resource system is calculated, and the results are shown in Figure 5.

As seen in Figure 5, in recent 7 years, 7 cities were at a good coordination level, 41 cities were at an intermediate coordination level, 52 cities were at a primary coordination level, 31 cities were at a barely coordinated level, 9 cities were at an imminent imbalance level, 3 cities were at a mild imbalance level, and 4 cities were at a moderate imbalance level. There were no cities at the extreme imbalance, serious imbalance, and quality coordination levels. Compared with the water resource–climate system, the number of cities in good coordination level increased by 4, the number of cities in intermediate coordination level increased by 24, the number of cities in primary coordination level increased by 35, the number of cities in barely coordination level decreased by 12, the number of cities in imminent imbalance level decreased by 24, and the number of cities in mild imbalance level decreased by 21. The number of cities in the moderate imbalance level is reduced by 5, the number in the serious imbalance level is reduced by 1, the coupling coordination level was greatly improved, and the level distribution was more concentrated.

3.4. Impact of Climatic Factors on the Water Resource System

According to the above analysis, it has been known that the coupling coordination degree of the water resource system shows an overall upward trend without considering the influence of climate factors. Therefore, this paper only selects data with a positive difference value for analysis, that is, the coupling coordination degree of a single water resource system is greater than that of the water resource–climate system. The impact degree of climate factors on water resources was characterized by the difference of the coupling coordination degree of the two systems, and the difference was denoted as $\Delta D$. In other words, the larger the $\Delta D$, the greater the degree of climate influence on water resource system; The smaller the $\Delta D$, the less the degree of climate influence on the water resource system.

3.4.1. Impact of Precipitation on the Water Resource System

When analyzing the influence of rainfall on water resource system, we found that the variation range of rainfall was large, so the logarithmic processing of rainfall was carried out to make the data series more stable. The calculation formula is as follows:

$$Y=\ln x,$$

(18)

In the above formula, $Y$ is the amount of rainfall after logarithmic transformation, and $x$ is the amount of rainfall during extreme precipitation or extreme drought events.

We fit $Y$ and $\Delta D$. When $R^2>0.8$ of the curve is fitted, we believe that the curve can represent the relationship between $Y$ and $\Delta D$. The specific results are shown in Figure 6 and Figure 7.

Effects of Extreme Precipitation Events on the Water Resource Systems

As shown in Figure 6, when extreme precipitation events occur, $Y$ and $\Delta D$ present a cubic polynomial relationship ($R^2=0.848$).

Figure 6. The influence curve of rainfall of extreme precipitation events on the water resource system.

Figure 6. The influence curve of rainfall of extreme precipitation events on the water resource system.

When the rainfall ranges from 789.7 to 916.0 mm, $\Delta D$ shows a downward trend with the increase in rainfall; that is, the greater the rainfall, the smaller the impact on the water resource system. The impact effect is that the rainfall increases by 1000 mm and the coupling coordination degree of the water resource system decreases by 0.48. When the rainfall ranges from 916.0 to 1958.6 mm, the $\Delta D$ shows an upward trend with the increase in rainfall; that is, the greater the rainfall, the greater the impact on the water resource system. The impact effect is that the rainfall increases by 1000 mm and the coupling coordination degree of the water resource system decreases by 0.11, which is consistent with the views of Srinivasan et al. [66]. They believe that increased rainfall will lead to a negative impact on agricultural production, resulting in increased pressure on agricultural water use. When the rainfall ranges from 1958.6 to 2161.3 mm, $\Delta D$ shows a downward trend with the increase in rainfall; that is, the greater the rainfall, the smaller the impact on the water resource system. The impact effect is to increase the rainfall by 1000 mm, and the coupling coordination degree of the water resource system decreases by 0.10.

Effects of Extreme Drought Events on the Water Resource Systems

As shown in Figure 7, when extreme drought events occurred, $Y$ and $\Delta D$ presented a quadratic polynomial relationship ($R^2=0.847$).

Figure 7. The influence curve of rainfall of extreme drought events on the water resource system.

Figure 7. The influence curve of rainfall of extreme drought events on the water resource system.

When the rainfall is between 668.9 and 972.6 mm, $\Delta D$ shows an upward trend with the decrease in rainfall; that is, the smaller the rainfall, the greater the impact on the water resource system. The impact effect is that the rainfall decreases by 1000 mm and the coupling coordination degree of the water resource system decreases by 0.96. When the rainfall ranges from 972.6 to 1394.1 mm, $\Delta D$ shows a downward trend with the decrease in rainfall; that is, the smaller the rainfall, the smaller the impact on the water resource system. The impact effect is that every 1000 mm decrease in rainfall, the coupling coordination degree of the water resource system decreases by 0.55, which is consistent with the conclusion of Aparicio et al. [67].

3.4.2. The Effect of Temperature on the Water Resource System

In the process of analyzing the influence of temperature on water resource system, we found that the change of temperature was relatively stable, and the temperature when extreme high temperature climate event or extreme low temperature climate event occurs is denoted as $T$. We fit $T$ and $\Delta D$. When $R^2>0.8$ of the curve is fitted, we believe that the curve can represent the relationship between $T$ and $D$. The specific results are shown in Figure 8 and Figure 9.

Impacts of Extreme High Temperature Events on the Water Resource Systems

As shown in Figure 8, when extreme high temperature events occur, the relationship between $T$ and $\Delta D$ is quadratic polynomial ($R^2=0.929$).

Figure 8. The influence curve of temperature on the water resource system under extreme high temperature events.

Figure 8. The influence curve of temperature on the water resource system under extreme high temperature events.

When the temperature is between 17.80 °C and 18.65 °C, $\Delta D$ shows a downward trend with the increase in temperature; that is, the higher the temperature, the smaller the impact on the water resource system. The impact effect is that the temperature increases by 1 °C, and the coupling coordination degree of the water resource system decreases by 0.14. When the temperature is between 18.65 °C and 19.18 °C, $\Delta D$ shows an upward trend with the increase in temperature; that is, the higher the temperature, the greater the impact on the water resource system. The impact effect is that the temperature rises by 1 °C, and the coupling coordination degree of the water resource system decreases by 0.072, which is consistent with the conclusion of Zhu et al. [68], who believe that under the condition of global warming. The water shortage situation in China’s nine water-scarce cities will further worsen.

Effects of Extreme Low Temperature Events on the Water Resource Systems

As shown in Figure 9, when extreme low temperature events occur, the relationship between $T$ and $\Delta D$ presents a quadratic polynomial ($R^2=0.869$).

Figure 9. The influence curve of temperature on the water resource system under extreme low temperature events.

Figure 9. The influence curve of temperature on the water resource system under extreme low temperature events.

When the temperature is between 17.33 °C and 17.48 °C, $\Delta D$ shows an upward trend with the decrease in temperature; that is, the lower the temperature, the greater the impact on the water resource system. The impact effect is that the temperature decreases by 1 °C and the coupling coordination degree of the water resource system decreases by 0.45. When the temperature is between 17.48 °C and 17.70 °C, $\Delta D$ shows a downward trend with the decrease in temperature; that is, the lower the temperature, the smaller the impact on the water resource system. The impact effect is that the temperature decreases by 1 °C and the coupling coordination degree of the water resource system decreases by 0.22.

3.4.3. Comprehensive Effects of Precipitation and Temperature on the Water Resource System

In extreme precipitation (high temperature) events, the end with large rainfall (temperature) is named as the extreme climate end, and the end with small rainfall (temperature) is named as the mild climate end. In extreme drought (low temperature) events, the end with small rainfall (temperature) is named as the extreme climate end, and the end with large rainfall (temperature) is named as the moderate climate end. In the analysis of the impact of precipitation on water resource system, it is found as follows: the extreme climate end of the fitted curve (Figure 6 and Figure 7) has a greater impact on water resources than the mild climate end. The effect of temperature on water resource system is analyzed as follows: the extreme climate end of the fitted curve (Figure 8 and Figure 9) has less impact on the water resource system than the mild climate end.

Based on the above two findings, we can draw the following conclusion; that is, when comprehensive extreme climate events occur in a certain region, one climatic factor is at the extreme climate end and the other is at the mild climate end, the coupling coordination level of the city may be high, which is consistent with the conclusion of Zhang et al. [69]. This explains the reason why the coupling coordination degree is still high in Bazhong City in 2021 when the comprehensive extreme climate of extreme precipitation and extreme low temperature occurs, and also shows once again that the process of evaluating the coupling coordination degree of a certain region should be viewed from a comprehensive perspective.

3.5. Study Limitations

For the analysis of the impact of the above climate factors on the water resource system, this paper has the following two limitations:

(1)

This paper considers only the case where $\Delta D$ is positive. By comparing the coupling coordination degree levels of the two systems, it is found that $\Delta D$ is negative in only a few areas. Through the analysis of these negative data, the main reason for this occurrence is caused by human activities and urbanization, so this part of data is not included in the analysis.

(2)

Due to the limited data acquisition, the conclusions obtained in this paper are suitable for short-term analysis, and it is not suitable to apply the conclusions to long-term future prediction.

4. Discussion

The results show that the water resource system of Sichuan Province is affected by extreme climate factors, and the coupling coordination degree of the water resource system is low when extreme climate events occur, which means that extreme climate factors will reduce the stability of the water resource system. This conclusion is consistent with the research results of Wubneh et al. [70], Sharan et al. [71] and Rankoana [72]. However, these scholars have described qualitatively the impact of extreme climate on water resource system, while this paper describes quantitatively the impact of extreme climate on water resource system. When calculating the coupling coordination degree, IFAHP and the improved CRITIC are used to determine the index weight, which increases the rationality and scientificity of system weighting compared with the original AHP [73] and CRITIC [74].

In the process of analyzing the coupling coordination degree, we find that when one factor is in the extreme climate and the other is in the mild climate, we may get a high coupling coordination level, which may be caused by the mutual influence of extreme climate factors. In previous studies [14,15,16], some scholars have suggested that extreme precipitation and high temperature waves are mutually promoting, and extreme precipitation generally occurs after high temperature heat wave events. Whether the interaction between extreme climate is the cause of the occurrence of comprehensive extreme climate events, and whether the effect of comprehensive extreme weather on the water resource system is stronger than that of a single extreme climate on the water resource system are questions that we should study and solve in the future.

In the selection of climate subsystem indicators, this paper mainly considers rainfall and temperature, and lacks the selection of other meteorological indicators (such as pressure, wind speed, wind direction, sunshine duration, solar radiation, etc.). Due to the limitation of data acquisition, the research period of this paper is short, and the identification of extreme climate events is not rich enough. In the future research will continue to improve the above deficiencies.

5. Conclusions

Extreme climate events occur more and more frequently around the world, so it is very necessary to study the impact mechanism of extreme climate on the water resource system. This paper identified the extreme climate events in Sichuan Province from 2007 to 2021, and studied the coupling coordination degree of the water resource–climate system and water resource system in Sichuan Province from 2015 to 2021. Based on the difference of the coupling coordination degree of the two systems, the effects of rainfall and temperature on the water resource system were investigated. The conclusions are as follows:

(1)

The five types of extreme events in Sichuan Province are listed in the order of frequency from large to small: extreme precipitation events > extreme drought events > extreme high temperature events = extreme low temperature events > comprehensive extreme climate events, and the extreme climate types of Sichuan Province in recent years have gradually transited from extreme drought to extreme precipitation, and from extreme low temperature to extreme high temperature.

(2)

In the past 15 years, Chengdu and Meishan are the cities with the most extreme climate events, while Deyang City and Tibetan Autonomous Prefecture of Garzê are the cities with the least extreme climate events. From 2015 to 2021, extreme precipitation events basically spread throughout Sichuan Province, extreme drought events mostly occurred in the northern and central parts of Sichuan Province, extreme high temperature events mostly occurred in the western, central and eastern parts of Sichuan Province, and extreme low temperature events only occurred in Guangyuan, Bazhong and Suining.

(3)

In recent 7 years, the coupling coordination degree of Sichuan Province was between serious imbalance and good coordination, among which the lowest coordination level occurred in Yibin City in 2016, and the highest coordination level occurred in Nanchong City in 2017, but most of the coupling coordination levels were between mild imbalance and intermediate coordination. The coupling coordination levels of the regions where extreme climate events occurred were all low, the lowest coordination level occurred in Yibin in 2016, where extreme precipitation events occurred, and the highest coordination level occurred in Bazhong in 2021, where the combined extreme climate events of extreme low temperature and extreme precipitation occurred, and most of the evaluation levels were between moderate imbalance and barely coordination.

(4)

The climatic factors of extreme drought, extreme high temperature and extreme low temperature are all related to $\Delta D$ quadratic polynomial, and only the rainfall of extreme precipitation events is related to $\Delta D$ cubic polynomial. When one climate factor is at the extreme end of the climate and the other is at the moderate end of the climate, a high coupling coordination level may be obtained, which explains the high coupling coordination level of Bazhong City where comprehensive extreme climate occurs in the previous conclusion.