Distribution Characteristics of Drought Resistance and Disaster Reduction Capability and the Identification of Key Factors—A Case Study of a Typical Area in the Yun–Gui Plateau, China

Abstract

Karst areas are characterized by poor surface water storage capacity, which makes them more sensitive to drought events. To enhance drought resistance in karst landform areas, this study focuses on a typical region in the Yun–Gui Plateau of China, specifically Guizhou Province, which includes 88 counties and districts. According to the regional characteristics, the index system for the assessment of drought resistance and disaster reduction ability was constructed to include 17 indexes in five evaluation layers, including natural conditions, water conservancy project, economic strength, water usage and water conservation level, and emergency support capacity. A comprehensive evaluation was conducted using a fuzzy evaluation model. Furthermore, the drought resistance and disaster reduction capacity of Guizhou Province was evaluated according to the fulfillment of water supply and water demand under the frequency of 75%, 90%, 95%, 97%, and 99% drought frequency inflow in each research unit. This assessment serves to define the spatial distribution pattern of drought resistance and disaster reduction capability within the province. Additionally, according to the results of the supply–demand balance method, the weight of the main influencing factors in regards to drought resistance and disaster reduction ability was optimized and adjusted to identify the key restricting factors of drought resistance and disaster reduction ability. This research data was obtained from the National Disaster Survey database, aiming to provide practical guidance for drought resistance in Guizhou Province. The research findings show that: (1) the distribution characteristics of drought resistance and disaster reduction capability in Guizhou Province are the most significant in Guiyang City, Liupanshui City, and Anshun City in the southwest, with higher drought resistance and disaster reduction ability found in central region, and lower drought resistance primarily identified in the eastern part of Qiandongnan Prefecture, Tongren City, the southern part of Qiannan Prefecture, and the northwestern part of Bijie City; (2) there are six main influencing factors in the three criterion layers, i.e., hydraulic engineering, emergency drought resistance, and social economy, and their contribution rates are as follows: surface water supply and storage rate > average number of soil moisture monitoring stations > per capita GDP > agricultural emergency drought irrigation rate > regional water supply assurance rate > cultivated land effective irrigation rate.

1. Introduction

The Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) from the United Nations reveals that the global average surface temperature increased by 1.09 °C during the period of 2011 to 2020 compared to that of the pre-industrial era of 1850 to 1900 [1,2]. In recent years, due to global warming and water resource shortages, the risk of compound high-temperature drought events may further increase [3,4,5]. The region around the Yun–Gui Plateau exhibits a subtropical monsoon climate and is characterized by the abundant distribution of limestone. Moreover, due to high precipitation levels and the erosive action of surface runoff, karst landforms are easily formed. However, southwest China is dominated by karst landforms, and the surface water storage performance is weak. This makes it more susceptible than surrounding regions to drought disasters [6]. From the autumn of 2009 to the spring of 2010, a drought occurred in five southwestern provinces of China, centered around Yunnan and Guizhou [7]. In particular, the drought event in southwest China from 2009 to 2012 lasted for a long time, affected a wide range, and caused a significant disaster [8]. In the summer of 2013, Chongqing, Sichuan, and Guizhou again experienced drought. Furthermore, in 2019 and 2020, Yunnan Province faced a severe drought, with temperatures reaching historic highs [9]. Guizhou, as a typical karst region [10], features extensive karst landforms. Additionally, its complex terrain, regional water scarcity, and limited underground water resources pose significant challenges for drought prevention and mitigation efforts in the province. Previous studies have shown a significant correlation between drought and the complex terrain and landforms of the region. The frequent occurrence of drought in Guizhou has also resulted in substantial losses to agricultural production and socioeconomic development in the area [11].

Currently, the research methods for assessing drought primarily involve the use of meteorological and hydrological drought indices [12,13] or comprehensive drought indices [14] to identify and evaluate drought events. These methods are enhanced by analytical techniques such as fuzzy mathematics [15], information diffusion theory [16], and the normal cloud model [17], all of which consider more comprehensive indicator systems. In the field of disaster risk reduction and assessment, the focus has historically been on earthquake mitigation [18], flood risk reduction [19], urban safety [20], and related areas. However, some researchers have begun to explore regional comprehensive disaster risk reduction and assessment efforts [21,22]. At present, most of these drought-related studies focus on risk, and most of them are only included in a guideline for comprehensive drought risk assessment. The research on earthquake resistance and flood control is developing gradually, but studies concerning drought resistance and disaster reduction are still relatively scarce. Drought events generally have a great impact on agriculture, so many scholars and researches studying drought resistance and disaster reduction mainly focus on the drought resistance of crop roots, such as wheat [23,24]; however, there have also been studies regarding drought relief and mitigation in hydraulic engineering studies focusing on features such as lakes and reservoirs [25,26], and there have been additional studies on drought mitigation [27]. Currently, research regarding drought resistance and disaster reduction capability is relatively limited, both domestically and internationally. Broadly speaking, there are two categories of research methods, The first approach is the use of a multi-indicator comprehensive assessment, as seen in the work of Gu Ying et al., who employed a fuzzy clustering method to categorize the drought resistance of Chinese agriculture [28]. This method combines relevant factors related to drought resistance and mathematical techniques for a systematic and comprehensive evaluation. Although it includes a broad range of factors, the lack of a unified standard for determining relevant indicators and their weights can lead to some degree of controversy regarding the reasonableness of the assessment results. The second approach is based on supply–demand balance analysis, as demonstrated by Jin Juliang et al., who utilized the coefficient of reservoir drought resistance assessment (CRDRA), calculated under various water inflow frequencies, to analyze regional drought resistance [29]. This method starts with the mechanisms of drought formation and to some extent, directly reflects the supply–demand relationships under different drought conditions in various counties and districts. It avoids the semi-quantitative nature of common multi-indicator comprehensive assessment methods, providing specific reference values for practical drought planning.

In the 2020 national comprehensive risk survey of natural disasters, the evaluation method for drought resistance and disaster reduction, as recommended in the pertinent technical documents of the Ministry of Water Resources of China, relies on an analysis of supply and demand balance. While the supply and demand balance approach offers a more intuitive and specific representation of overall regional conditions, it fails to capture the contribution of the influencing factors in regards to drought-resistance in areas characterized by complex factors, such as karst landforms. Although in the current evaluation research, the comprehensive evaluation method is widely used, it cannot be guaranteed because of its index diversity, weight, and the rationality of the results. Especially in the field of drought resistance and disaster reduction capacity assessment, the previous research mainly focused on comprehensive regional agriculture assessment needs to build a new index system and create a reasonable division standard. At present, the study of drought resistance and the system of disaster reduction capability assessment is not mature, and these two methods each have their advantages and disadvantages. Therefore, it is necessary to further explore the evaluation system of drought resistance and disaster reduction capability in order to improve the prevention and control of drought in karst regions.

This study hopes to directly reflect the spatial distribution of the drought resistance capacity of the study area through the supply–demand balance method, and to enhance the disaster reduction capacity evaluation by combining it with the diversity of the fuzzy evaluation method; thus, the aim of drought resistance and disaster reduction capability evaluation and key factors identification can be achieved. Therefore, against the background of global warming, this paper specifically focuses on 88 counties in Guizhou Province, a typical region of the Yun–Gui Plateau, China. The index system for evaluating drought resistance and disaster reduction capability was established according to regional characteristics such as karst, and the drought resistance and disaster reduction capability was evaluated based on the supply–demand balance method. Finally, the index weight of the fuzzy comprehensive evaluation method is modified based on the evaluation results to obtain the contribution rate of the impacting factors. Based on the integrity of the administrative boundaries, this study aims to construct an index system to evaluate the drought resistance and disaster reduction abilities and identify the key factors necessary for enhancing these abilities. The aim of the study is to identify the drought prevention capacity of Guizhou Province and provide scientific guidance for regional drought planning in the karst.

2. Study Area Profile and Data Sources

2.1. Overview of the Study Area

Guizhou Province is located in the southeastern part of southwestern China (103°36′–109°35′ E, 24°37′–29°13′ N). The province spans approximately 595 km from east to west and covers a total area of 176,200 km², accounting for 1.84% of the total land area of the country [30]. About 73.8% of the province’s area is characterized by a karst topography, making it the region with the widest distribution of karst landforms in the world. Guizhou Province is administratively divided into six prefecture-level cities and three autonomous prefectures, comprising a total of 88 county-level administrative divisions. The province is situated in both the Yangtze River Basin and the Pearl River Basin. Its climate is classified as subtropical humid monsoon, with an annual precipitation ranging from 1100 to 1400 mm, although the distribution of rainfall can be uneven across both time and space [31]. The terrain generally slopes from west to east, and the frequency of drought occurrences follows a similar spatial pattern. In 2011, Guizhou Province experienced increased drought conditions, particularly in areas with intense karst landscapes. These regions have limited surface water retention capacity, leading to severe soil erosion and negative impacts on agricultural production [32]. An overview of the research area is depicted in Figure 1.

2.2. Data Sources

This calculation was based on the results of the third national water resources survey in Guizhou Province from 1956 to 2016 and the water resources data from the investigation of drought disasters in Guizhou Province from 2017 to 2020. Social and economic data, such as meteorological and hydrological data, population figures, economic data, and the proportion of cultivated land, primarily came from the third national water resources survey in Guizhou Province, the first national drought disaster risk survey in Guizhou Province, and the Guizhou Statistical Yearbook (2021). The DEM (digital elevation model) data was obtained from the Geographic Spatial Data Cloud (http://www.gscloud.cn, accessed on 20 July 2023), with a resolution of 30 m. Water use quotas were based on local standards, specifically the Water Use Quotas. Spatial distribution maps were created using ArcGIS 10.1.

2.3. Research Ideas

Guizhou Province has a complex topography and is the only major agricultural province in China without significant plains. Its susceptibility to drought is influenced by a variety of factors. Therefore, a comprehensive assessment of drought resistance and disaster reduction capability has been constructed using the fuzzy comprehensive evaluation method. This assessment system consists of five criterion layers, including natural conditions, hydraulic engineering, economic strength, water usage and water conservation level, and emergency drought resistance capabilities. It encompasses 17 evaluation indicators that have a specific impact on drought resistance and disaster reduction capability. The fuzzy theory is employed to perform a comprehensive evaluation. Subsequently, an analysis of water supply capacity and water demand under different drought frequencies for different research units is conducted to determine the spatial distribution of drought resistance and disaster reduction capability in Guizhou Province. This information is used to optimize the weighting of the comprehensive evaluation indicators. Finally, by adjusting the fitted weighting coefficients, the contribution rates of the influencing factors are analyzed, and the analysis is carried out in combination with related factors. The evaluation flow chart is shown in Figure 2.

3. Evaluation Method

3.1. Fuzzy Comprehensive Evaluation Method

The fuzzy comprehensive evaluation method is a comprehensive assessment approach based on fuzzy mathematics [33]. This method transforms qualitative evaluations into quantitative assessments using the principles of fuzzy mathematics. In essence, it employs fuzzy mathematics to provide an overall evaluation of entities or objects influenced by multiple factors. Combined with AHP, the complex problem is divided into a target layer, a criterion layer, and an index layer, and a multi-level analysis framework pattern is formed through a classification layer.

3.1.1. Index System Construction

In this study, the drought resistance and disaster reduction abilities were divided into five grades: low, medium-low, medium-high, medium-high, and high. The ArcGIS natural demarcation point method was adopted for grade classification and revised in combination with expert evaluation [34]. The corresponding dividing standards for the indicators is shown in

Table 1. Standard table of criterion layer index classification.

Criterion Layer	Index Layer	Low	Medium-Low	Medium	Medium-High	High
Natural condition	rocky desertification degree (%)	≤20	20–40	40–50	50–60	>60
	average elevation (%)	≤20	20–40	40–60	60–70	>70
	runoff depth negative anomaly index	≤50	50–60	60–70	70–80	>80
	vegetation coverage (%)	≤20	20–40	40–60	60–80	>80
Water conservancy project	surface water supply and storage rate (%)	≤20	20–40	40–50	50–60	>60
	cultivated land effective irrigation rate (%)	≤30	30–50	50–70	70–90	>90
	regional water supply assurance rate (%)	≤60	60–80	80–100	100–150	>150
	rural centralized water supply projects population coverage rate (%)	≤60	60–70	70–80	80–90	>90
Economic strength	per capita GDP (10,000/person)	≤3	3–4	4–5	5–7	>7
	urbanization rate (%)	≤40	40–60	60–80	80–90	>90
Water usage and water-saving level	water-saving irrigation rate (%)	≤30	30–50	50–65	65–80	>80
	dry land crop planting proportion (%)	≥70	50–70	40–50	30–40	<30
	agricultural water allocation ratio (%)	≥70	55–70	40–50	20–40	<20
	irrigation water effective utilization coefficient	≤0.47	0.41–0.48	0.48–0.49	0.49–0.5	>0.5
Emergency drought resilience capacity	average number of drought service personnel per unit area (person/20,000 ha)	≤15	15–20	20–25	25–30	>30
	agricultural emergency drought irrigation rate (%)	≤40	40–60	60–80	80–90	>90
	average number of soil moisture monitoring stations (piece/50,000 ha)	≤2	2–4	4–6	6–8	>80

.

3.1.2. Preliminary Formulation of the Weights of Indicators

The data regarding the indicator level were normalized, and the entropy weight method [35] was employed to calculate the weights of these indicators. Then, using the analytic hierarchy process (AHP) [36], the weights were further computed to obtain the weight vector U. The summary of the weights of the criterion layer and the indicator layer can be found in

Table 2. Summary of the weights of the preliminary criterion layer and the index layer.

Criterion Layer	Natural Condition		Water Conservancy Project		Economic Strength		Water Usage and Water-Saving Level		Emergency Drought Resilience Capacity
	0.041		0.123		0.156		0.132		0.548
index layer	rocky desertification factor	0.47	surface water supply and storage rate	0.62	per capita GDP	0.69	water-saving irrigation efficiency	0.32	average number of drought service personnel per unit area	0.19
	average elevation	0.20	cultivated land effective irrigation rate	0.15	urbanization rate	0.31	dry land crops planting proportion	0.25	agricultural emergency drought irrigation rate	0.19
	runoff depth negative anomaly index	0.21	regional water supply assurance rate	0.20			agricultural water allocation ratio	0.35	average number of soil moisture monitoring stations	0.62
	vegetation coverage	0.12	rural centralized water supply projects population coverage rate	0.03			irrigation water effective utilization coefficient	0.07

.

3.1.3. Comprehensive Evaluation

1.

Membership Degree Matrix

The membership function is calculated according to the types of indicators, and each evaluation indicator belongs to the membership matrix of each evaluation level [37]. The membership function was calculated according to the index types, and each evaluation index belongs to the membership matrix of each evaluation grade. The membership matrix was calculated as follows:

$$R=\left[\begin{array}{cccc}{r}_{11}& r_{12}& \cdots & r_{1j}\\ r_{21}& r_{21}& \cdots & r_{2j}\\ ⋮& ⋮& \ddots & ⋮\\ r_{m1}& r_{m2}& \cdots & r_{mn}\end{array}\right]$$

(1)

In the formula, r_ij is the i index belonging to the j grade’s membership degree.

2.

Fuzzy vectors

The fuzzy vector W represents the comprehensive membership, which is obtained by multiplying the membership matrix R and the weight vector U. The fuzzy vector is calculated as follows:

$$W=R·U$$

(2)

3.

Evaluation criteria

The criterion layer evaluation classifies the indicator system into five categories: natural conditions, water conservancy project, economic strength, water usage and water conservation level, and emergency support capacity. The comprehensive score is the minimum of the score obtained from the criterion layer, and the formula is as follows:

$$P=W·F$$

(3)

In the formula, the score level matrix F is represented as (20 40 60 75 100)^T. In this matrix, a score of 100 represents the highest drought resistance and disaster reduction capability, while a score of 20 represents the lowest. The score classification standards are defined as {high, medium-high, medium, medium-low, low} = {65, 60, 55, 50}.

3.2. Supply and Demand Balance Forecasting

3.2.1. Water Availability

The adjustment coefficient method was adopted to calculate the available water supply, and the frequency of water resources was calculated based on the data regarding water resources for many years. Empirical frequency scheduling and P-III curve fitting were performed for the calculation frequency and water resources series [38] (the coefficient of variation (Cv) and the coefficient of skew (Cs) were adjusted to achieve the best fitting effect on the whole). The empirical frequency ranking formula [39,40] is as follows:

$$P=\frac{i}{n+1}\times 100\%$$

(4)

where i denotes the sequence number after frequency ranking; n denotes the total length of the sequence; and P denotes the frequency.

The adjustment coefficient method was used to calculate the available water supply on the basis of the water resource data from the third national water resource survey in Guizhou Province from 1956 to 2016, as well as the water resource data from the drought disaster investigation in Guizhou Province from 2017 to 2020. According to Equation (1), an empirical frequency ranking was performed, and the P-III curve was used to fit the water resource quantity at 75%, 90%, 95%, 97%, and 99% frequency levels of 88 counties and districts in Guizhou Province. The water resource quantity at the frequency levels of 75%, 90%, 95%, 97%, and 99% were linearly fitted with that of the 75% frequency level, and the correlation coefficient k between each frequency and the water supply capacity at 75% of the incoming frequency was obtained. The fitting results are shown in Figure 3. Since the linear correlation coefficients (R²) of the fitted data are all greater than 0.95, it is considered that the fitting of this data is highly reliable. Therefore, the water supply capacity adjustment coefficients are as follows: Y_90% = 0.8381 × X_75%; Y_95% = 0.7499 × X_75%; Y_97% = 0.6959 × X_75%; Y_99% = 0.6009 × X_75%.

Based on the analysis of the water supply frequency, it is known that in 2018, the water supply frequency in Guizhou Province was close to 75%, and the water infrastructure and facilities were close to their current state. Therefore, the water supply at other frequencies is calculated based on the multi-year average water supply data for various types of water supply projects in 2018. The available water supply S of each evaluation unit in Guizhou Province in the actual year at 90%, 95%, 97%, and 99% frequency were obtained according to the adjustment coefficient.

3.2.2. Water Demand Calculation

The water demand includes domestic water demand, agricultural water demand, industrial water demand, construction and tertiary industry water demand, and ecological environmental water demand. Based on historical drought losses and the relevant research, it is evident that agriculture and human consumption are significantly affected by drought. With reference to the water requirements calculated in the 2010 drought plan and the actual water requirements for the 75% frequency inflow year (2018), and based on the fact that the water demand of industries other than agriculture remains the same under different drought frequencies, it is only necessary to adjust the domestic water demand and agricultural water demand under different inflow frequencies, according to the quota method.

1.

Water Requirement

The calculation of daily life water demand uses quotas [41], including the urban population water demand and the rural population water demand; the calculated formula is as follows:

$$L=L_u+L_r=Q_u\times N_u\times 365+Q_r\times N_r\times 365$$

(5)

where L represents living water demand; u represents urban demand; r represents rural demand; Q denotes quota; the urban population water consumption quota is 110 L per person per day; while the water quota for rural population is 80 L per person per day [42].

2.

Water for Agriculture Needs

According to the characteristics of this study area, the agricultural water demand was calculated based on the main crops grown in Guizhou, i.e., wheat, corn, rape seed, and flue-cured tobacco. The calculation formula is as follows:

$$A=\frac{\sum （a\times Q）\times F_i\times F_f}{F_a}$$

(6)

where a represents the effective irrigation area; Q represents the quota value of crop water consumption. According to the irrigation zone, Class II (general value) is adopted as the maximum allowable value of current project water consumption. F_i represents the correction coefficient of the irrigation mode, and the field irrigation mode is soil channel irrigation, taking 1.00; F_f represents the correction factor of facility condition, and the facility condition is open air, which is 1.00; F_a represents the basic adjustment coefficient of agricultural irrigation water, and the 2020 year level is 0.486 [42].

3.2.3. Calculation of Supply–Demand Ratio

The supply–demand ratio is a significant indicator for studying the balance between water resources supply and demand [43]. In this study, the supply–demand ratio indicator is calculated at the county level,

$$R=\frac{S}{N}$$

(7)

where R denotes the supply–demand ratio indicator, S denotes the water supply, and N denotes the water demand. R = 1 means supply and demand balance; R > 1 means that supply exceeds demand and meets the demand of water supply under this frequency; R < 1 means that supply is less than demand, and does not meet the demand of water supply under this frequency.

3.2.4. Rules for Evaluating Drought Resistance and Disaster Reduction Capability

The assessment of drought resistance capacity using the supply–demand balance method primarily considers whether the water supply can meet the current water demand under different drought frequencies (75%, 90%, 95%, 97%, 99%). This serves as the basis for classifying drought resistance capacity levels. The classification rules for drought resistance capacity refer to the Technical Specifications for the First National Natural Disaster Risk Survey—Technical Requirements for Drought Disaster Risk Investigation, Assessment and Zoning (Trial) (FXPC/SL D-05). According to the supply–demand ratio, whether the water supply can meet the demand under a specific drought frequency can be determined, then the drought resistance capacity level is classified accordingly [29]. The specific rules are shown in

Table 3. Classification table of drought resistance and disaster reduction ability.

Drought Frequency	Once in Five Years		Once in Ten Years	Once in Twenty Years	Once in Fifty Years
supply–demand ratio	<1	≥1	≥1	≥1	≥1
level	low	low-medium	medium	medium-high	high

.

3.3. Spatial Class Transfer Matrix

The Markov process, proposed by Russian mathematician A.A. Markov in 1907, is a type of stochastic process [44]. Because there can be more than one state at each different moment in time, there are several possibilities for transitioning from the previous state at one time to a certain current state. In this case, all conditional probabilities will form a matrix, which is known as the transition probability matrix [45]. Markov models not only quantitatively demonstrate the transitions between different states, but also reveal the transition rates between different types. In this paper, the transition matrix was utilized to provide a quantitative description of the state of transition and the state of each level area (X_i = t) using the supply and demand balance method and the fuzzy evaluation method (X_i−1 = s). This description reveals the transition conditions between the two methods, and the formula is as follows:

$$P\_st=P(X\_i=t│X\_(i-1)=s)$$

(8)

4. Results and Discussion

4.1. Spatial Distribution Characteristics of Drought Resistance and Disaster Reduction Capability

The supply–demand balance relationships under different drought frequencies in the current year serve as the basis for classifying drought resistance capacity levels. According to the calculation method described in Section 2.2 and the classification rules in Table 1, the calculated supply–demand ratios for each county or district are used to determine whether the water supply can meet the demand under various drought frequencies. This assessment contributes to the evaluation of the drought resistance capacity levels. By combining this assessment with ArcGIS mapping, a distribution map showing drought resistance capacity levels based on supply–demand balance predictions was drawn, as depicted in Figure 4. The results indicate that areas with generally lower drought resistance capacity in Guizhou Province are primarily located in the northwest, northeast, southeast, and southern regions. Regions with higher drought resistance capacity are concentrated in central areas, like Guiyang City, southwestern regions including Anshun City and Liupanshui City, and developed zones in Qianxinan and other areas.

4.2. Fuzzy Comprehensive Evaluation Method and Correction

4.2.1. Adjustment of Index Weight

Fuzzy comprehensive evaluation often uses the maximum membership principle to derive assessment results. To calculate the membership matrix for indicators, based on the defined levels, and combine it with the preliminary weights from Equation (4) and Table 3, the comprehensive membership for various evaluation units related to the indicators can be obtained [46]. Criterion level evaluation is calculated according to Formula (5), and the comprehensive score of the target level evaluation is the minimum value of the criterion level score. Then, the drought resistance and disaster reduction abilities can be determined, according to the corresponding scores of the scoring set. If the comprehensive score of a particular evaluation unit is closest to a score in the scoring set mentioned earlier, the drought resistance capacity is determined to be at the corresponding level.

Because the supply–demand balance method provides a macroscopic view of how well water supply meets demand under regional drought conditions, it can visually show a region’s drought resistance capacity. Therefore, it is used to fit the preliminary fuzzy comprehensive evaluation results with the drought resistance capacity levels obtained through the supply–demand balance method. By modifying the weight of the index, the sum of the area occupied by the same evaluation grade reaches 60% or more. By adjusting the weight of the indicators, the sum of the areas corresponding to the same evaluation grade reaches 60% or more. The index system obtained by the fuzzy evaluation method is reasonable, as shown in

Table 4. Summary table of the revised criterion layer and the indicator layer weights.

Criterion Layer	Natural Condition		Water Conservancy Project		Economic Power		Water Usage and Water Conservation Level		Emergency Preparedness Capacity
	0.060		0.370		0.160		0.140		0.270
index layer	rocky desertification degree	0.38	surface water supply and storage rate	0.62	per capita GDP	0.80	irrigation efficiency	0.32	average number of drought service personnel per unit area	0.17
	average elevation	0.20	cultivated land effective irrigation rate	0.15	urbanization rate	0.2	dryland crop planting ratio	0.25	agricultural emergency drought irrigation rate	0.30
	runoff depth negative anomaly index	0.12	regional water supply assurance rate	0.20			agricultural water allocation ratio	0.35	average number of soil moisture monitoring stations	0.53
	vegetation coverage	0.30	rural centralized water supply project population coverage rate	0.03			irrigation water effective utilization coefficient	0.08

. By integrating this adjusted evaluation with ArcGIS mapping, a spatial distribution map of the results from the fuzzy comprehensive evaluation was drawn, as depicted in Figure 5.

4.2.2. Rationality Analysis

Using spatial analysis tools, the transition process between different levels of drought resistance capacity as reflected in the research units was quantitatively calculated in a transition matrix, as shown in

Table 5. Transition probability matrix of drought resistance and disaster reduction ability levels in Guizhou Province.

Drought Resistance and Disaster Reduction Capability Level Area Ratio (%)		Fuzzy Evaluation Method						Reduction (∇)
		Low	Low-Medium	Medium	Medium-High	High	Total
supply–demand balance method	low	57.72	0.00	0.00	0.00	0.00	57.72	0.00
	low-medium	14.59	2.52	1.10	0.00	0.59	18.80	16.28
	medium	6.89	2.59	0.00	0.00	1.15	10.63	10.63
	medium-high	1.28	0.61	0.00	0.00	0.61	2.50	2.50
	high	1.65	0.00	1.46	5.26	1.98	10.35	8.38
	total	82.13	5.73	2.56	5.26	4.32	100.00	37.79
Promotion (Δ)		24.41	3.21	2.56	5.26	2.35	37.79	——

(where the rows in the transition matrix represent drought resistance capacity levels based on the fuzzy evaluation method, and the columns represent levels based on the supply–demand balance method). The elements on the diagonal of the state transition probability matrix represent the probability of transition between the same levels using both methods, while the off-diagonal elements represent the probability of transitioning between different levels [47,48]. Table 5 shows that in the low-level transitions, the probability values on the diagonal are greater than the off-diagonal probability values, indicating some stability between these levels. For example, the transfer area from low to low accounted for 14.59%, and the transfer area from high to high accounted for 5.26%. However, in the transitions between low and medium-low and between high and medium-high, the probability values on the diagonal are smaller than the off-diagonal values, suggesting higher variability involved in the transitions between these levels.

The difference may be due to the fact that the water supply and demand conditions correspond to the results of a combination of natural, hydraulic engineering, socio-economic, and water-saving conditions; however, the fuzzy evaluation method only selects more representative indexes, and the two indexes cannot reach a complete coincidence. At the same time, the adjustment of the index weight is holistic, and there may still be individual differences for each research unit. Therefore, Table 5 shows that the ratio of grade area on the diagonal is 62.21%, which is greater than 60%.

4.3. Identification of Main Influencing Factors of Dought Resistance and Disaster Reduction

4.3.1. Analysis of Contribution Rates of Influencing Factors

The contribution rates of the influence factors of the fuzzy comprehensive evaluation method were obtained by the weighted summation of modified index weights and criterion layer weights, as shown in

Table 6. Impact factor contribution rate table.

Number	Indicators	Contribution Rate	Number	Indicators	Contribution Rate
1	surface water supply and storage rate	22.93%	10	dryland crop planting ratio	3.50%
2	average number of soil moisture monitoring stations	14.31%	11	urbanization rate	3.20%
3	per capita GDP	12.80%	12	rocky desertification degree	2.28%
4	agricultural emergency drought irrigation rate	8.10%	13	vegetation coverage	1.80%
5	regional water supply assurance rate	7.40%	14	average elevation	1.20%
6	cultivated land effective irrigation rate	5.51%	15	rural centralized water supply project population coverage rate	1.16%
7	agricultural water allocation ratio	4.90%
8	average number of drought service personnel per unit area	4.59%	16	irrigation water effective utilization coefficient	1.12%
9	irrigation efficiency	4.48%	17	runoff depth negative anomaly index	0.72%

. In this fuzzy comprehensive evaluation, a total of 17 relevant indicators was selected. Among them, there were six indicators with contribution rates exceeding 5%, including surface water supply and storage rate, the number of average soil moisture monitoring stations, per capita GDP, agricultural emergency drought irrigation rate, regional water supply guarantee rate, and cultivated land effective irrigation rate. These six indicators were considered as the main influencing factors. Three of them belonged to the water conservancy project, two were emergency drought resistance capacities, and one belonged to the socio-economic criterion, with influencing weights of 0.37, 0.27, and 0.16, respectively. The score of the criterion layer of fuzzy evaluation method was spatially distributed, as shown in Figure 6. Based on the spatial distribution of scores for natural conditions, water engineering, and economic strength shown in Figure 6a–c, it can be observed that the water engineering construction in the central part of Guizhou Province is relatively well-established. Additionally, the central and northeastern regions, including Zunyi and Tongren, are relatively economically developed, with higher urbanization levels contributing to overall higher drought resistance capacity. Research has shown that human activities have a certain impact on the formation of drought [49], and drought has a particularly severe impact on agriculture. Figure 6d,e shows water usage and water conservation levels, and according to the emergency drought management score, it becomes apparent that agricultural water use, water conservation, and emergency management are crucial components of drought resistance.

Guizhou has a subtropical monsoon climate. Due to monsoon instability, uneven precipitation distribution, large inter-annual variability, and karst characteristics, the surface water storage capacity is weak; therefore, the construction and improvement of regional hydraulic engineering facilities and related supporting structures is vital. The central region, as an highly developed economic area, exhibits good overall drought resistance and disaster reduction capacity, but for the more remote areas, due to the complex topography, there are difficulties in maintaining the water supply when severe drought occurs; therefore, these areas should pay more attention to their emergency drought relief investment when they encounter drought events. As shown by the water-saving level of the agricultural emergency irrigation rate in regards to drought resistance, the effective farmland irrigation rate, the proportion of agricultural water use, and the water-saving irrigation rate of the four indicators in the top 50% of contributors, drought shows a significant impact on crops in Guizhou, since it is a large agricultural province; therefore, the distribution of agricultural water must be adjusted and optimized under drought events.

4.3.2. Countermeasure Analysis

As an agriculture-oriented province, agriculture and a rural system have always been an important part of Guizhou’s economy. In the 14th Five-Year Water Development Plan released by Guizhou Province in 2020 [50], it was noted that there are regional and engineering water shortage issues. Therefore, the direction of drought management not only involves focusing on water engineering construction in regions with a lower drought resistance capacity, but also includes advancing an urban–rural unified water supply and emergency backup water source projects. In urban and rural planning, efforts are being made to establish a three-tier drought service network at the county, township, and village levels, centered around county-level drought service organizations. This aims to improve the drought service capacity of the entire region and address rural drinking water safety issues.

While meteorological factors constitute the source of the foundational risk of drought disasters in Guizhou Province [51], the evolution of droughts is significantly influenced by regional water resources. Among the various river basins in Guizhou, the Wujiang River Basin covers the largest area, with abundant water resources [52]. In contrast, other basins comprise relatively smaller areas, and the connectivity of their water networks is a crucial factor in regional drought resistance. Therefore, it is essential to expedite the development of key water source projects and large-scale water network construction. This includes the targeted implementation of national and provincial water resource development plans, such as the New State Council Document No. 2 and the 14th Five-Year Water Development Plan for Guizhou. The focus should be on projects that promote connectivity within river and lake systems, thereby enhancing the pattern of water resource allocation and improving the capacity to combat water and drought disasters [53]. Furthermore, in addition to further improving basin and regional water networks and water engineering projects, there should be a strong emphasis on the development of infrastructure to enhance drought resistance. This involves strengthening non-engineering measures, particularly those aimed at mitigating major drought risks. These measures should focus on early warning, forecasting, and scheduling as their primary objectives. It is crucial to establish a risk management system for water and drought reduction in river basins, including an adaptive management system [54], a regional comprehensive collaborative drought relief and a disaster reduction system based on multiple water-saving, water-storage, water-regulation, methods, as well as multiple air, space, and ground monitoring technologies.

5. Conclusions

The traditional evaluation of drought resistance and disaster reduction capacity is divided into the supply and demand balance method and the comprehensive evaluation method. The results obtained by the method of supply and demand balance are more realistic, but their comprehensiveness cannot reflect the effect of other factors in the whole process of drought; because of the different indexes in different regions, the weight calculation can only be realized using mathematical statistics, but the rationality of the result is questionable. In this study, a multi-index system of drought resistance and disaster reduction capability was established by using the fuzzy evaluation method, which was applied to the Yun–Gui Plateau. The drought resistance and disaster reduction capability grades for each research unit were obtained through supply and demand balance analysis; simultaneously, according to the results of the supply–demand balance method, the weight of comprehensive evaluation was determined, and the key influencing factors were identified. The method combines the rationality of the supply–demand balance method with the diversity of the comprehensive evaluation technique, and the evaluation is proven to be easy to operate and can be applied to the drought-related assessment in the Yun–Gui Plateau Region. Because of the limited data and the complexity of the index system, the final proportion of the same grade area is only 62%, requiring further optimized. As a dominant factor of drought, climate factors should be evaluated to determine their impact on drought resistance and disaster reduction capacity in a follow-up study. The main conclusions of this paper are as follows:

• The drought resistance and disaster reduction capacities of Guizhou Province are determined based on whether or not the water supply can meet the water demand under different drought frequencies. The results show that the areas with lower drought resistance and reduced disaster capacity in Guizhou Province are mainly located in the northwest, northeast, southeast, and some southern areas. The central, northeast, and southwest regions, such as Zunyi and Tongren, exhibit a relatively developed economy, relatively effective water conservancy project construction, and a relatively high urbanization level, resulting in higher overall drought resistance and disaster reduction capacity.
• The evaluation results in regards to drought resistance and reduction capacity according to the supply and demand balance method were taken as the reference object. This is helpful to determine the index weight of the fuzzy comprehensive evaluation method in order to ensure the accuracy of identifying the factors impacting drought resistance and disaster reduction ability. The ranking of the contribution rates of the influencing factors is as follows: surface water supply and storage rate > average number of soil moisture monitoring stations > per capita GDP > agricultural emergency drought irrigation rate > regional water supply assurance rate > cultivated land effective irrigation rate > agricultural water allocation ratio > average number of drought service personnel per unit area > irrigation efficiency> dryland crop planting ratio > urbanization rate > rocky desertification degree> vegetation coverage > average elevation > rural centralized water supply project population coverage rate > irrigation water effective utilization coefficient > runoff depth negative anomaly index. Therefore, it is considered that in terms of water conservancy projects, economic strength, and emergency drought criterion, the six key indicators that primarily affect drought resistance and disaster reduction capabilities are surface water supply and storage rate, average number of soil moisture monitoring stations, per capita GDP, agricultural emergency drought irrigation rate, regional water supply guarantee rate, and cultivated land effective irrigation rate.