The regional water cycle is increasingly reflecting the dual role of natural and social processes, and is affected by global climate change and strong human intervention. The study of water cycle health evaluation has provided guidance to urban planning, development, and resource management. In this study, a water cycle health evaluation method based on EFAST-Cloud model is proposed, and Henan Province of China is selected as a typical study area. The water cycle health status is evaluated from four dimensions of water ecology, water quality, water abundance, and water utilization. The evaluation results are compared with those of fuzzy comprehensive evaluation as well. It is shown that the established EFAST-Cloud model results of assessment are consistent with the Fuzzy comprehensive evaluation method, while it has the advantage of being able to evaluate the proportion of each grade in detail by reflecting the randomness and fuzziness of the evaluation. According to the statistical data from 2007 to 2018, the whole area is in a sub-healthy state. During the 12-year period, the health status of the water cycle has been improved year by year. The research results can provide theoretical basis for repairing regional water environment and promoting regional sustainability.

  • A water cycle evaluation index system with 18 indicators including four criteria of water ecology, water quality, water abundance and water utilization is constructed.

  • EFAST-Cloud model is innovatively proposed and applied in regional water cycle health evaluation.

  • The health status of water cycle in Henan Province was sub-healthy from 2007 to 2018 and showed an increasing trend year by year.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Water resources can be recycled. The water cycle is important in maintaining sustainable development. With the rapid development of urbanization, the structure and parameters of natural water cycle have gradually changed (Wang et al., 2006). Objective evaluation of the water cycle health status is conducive to the development of scientific and rational water management measures. Scholars and policymakers have debated the environmental and social implications of this change, such as water resources shortage, water pollution, and water ecological degradation in many regions (Brown et al., 2010; Chen et al., 2012; Wang et al., 2017; Janjua et al., 2020). Maintaining the health of regional water cycle is an essential environmental and ecological guarantee for regional sustainability.

As one of the most important material cycles globally, the water cycle realizes the migration and conversion of water, energy, and biochemicals. The water cycle process involves multiple levels, such as water source, water intake, water supply, and water use. The health status of the water cycle is closely related to all sub-processes. In recent years, regional water cycles have increasingly reflected the dual influences of natural and social processes. Under the influence of climate change and human activities, changes in the water cycle are becoming increasingly significant. Comprehensive evaluation of water circulation health status is related to sustainable utilization and benign development of water resources, which has aroused great attention of scholars and government departments (Amores et al., 2013; Uche et al., 2015). Understanding how to maintain a healthy state of the water cycle has become an important proposition for sustainable development of human society.

The reasonable establishment of evaluation index system plays an important role in the accuracy and objectivity of regional water circulation health evaluation. Based on the fuzzy set theory, Zhang et al. (2017) integrated the natural and social attributes of the water cycle, selected 19 evaluation indexes from four dimensions of water ecosystem integrity, water quality, water resource abundance, and water resource utilization, and established a water cycle health grading evaluation system. Tang et al. (2019) constructed an urban water cycle health evaluation system based on key performance indicators (KPI) from four dimensions: water ecology, water abundance, water quality, and water utilization, and evaluated the water cycle health in Xi'an. Wang et al. (2021) constructed a water cycle health evaluation index system covering water resources, water environment, water ecology, water utility, and water disaster, and the water cycle health of Beijing-Tianjin-Hebei region was evaluated using the fuzzy comprehensive evaluation method. Huyghe et al. (2021) selected 18 indexes to construct the trend pressure framework from the aspects of society, environment, finance, and government, the urban water cycle and sustainability in Antwerp, Belgium were evaluated.

At present, there are great differences in the objects and methods of water cycle health evaluation. In terms of evaluation objects, previous studies on water cycle health evaluation tend to focus only on one of the natural and social attributes of the water cycle. Natural attributes include water cycle health assessment of wetlands, lakes, and rivers (Noble et al., 2010; Qi et al., 2013; Zhang et al., 2016), while another type of water cycle assessment focuses on social attributes of the water cycle, mainly involving sub-processes of water cycle such as water supply, water use, and tap water reuse (Chen et al., 2012; Vilanova et al., 2015; Lu et al., 2016). The dual attribute of water cycle makes water cycle health assessment involve more dimensions and levels. How to comprehensively analyze the weak links in the evaluation of water cycle is an urgent problem to be solved today. In terms of research methods, in order to get accurate water cycle health evaluation, many evaluation methods have been adopted for evaluation of the health status of the water cycle, such as the composite-index method (Wirehn et al., 2015; Cookey et al., 2016; Yan et al., 2016), fuzzy recognition method, fuzzy comprehensive method, and fuzzy clustering method. Those methods are based on fuzzy sets (Zadeh, 1965), and the cloud model method (Li et al., 1995). However, an appropriate evaluation method must be chosen to study the influence of both the random error of an evaluation index and the impact of threshold fuzziness on derived evaluation conclusions to obtain evaluation results that are more objective and meticulous.

In view of current problems in the evaluation of water cycle health, this paper focuses on the following aspects: (1) proposing an EFAST-Cloud model comprehensive evaluation method considering the coupling effect, index sensitivity, and uncertainty of evaluation, and applying it to the water cycle health evaluation creatively; (2) accounting for natural and social dualistic characteristics of the water cycle and proposing a composite-index evaluation system for regional with 14 indicators was constructed, covering four criteria levels of water ecology, water quality, water abundance, and water utilization; (3) defining weights and a threshold range of evaluation indexes for a specific region; (4) evaluating the health status of the water cycle based on the cloud model, whereby the evaluation results were compared with results obtained using the fuzzy comprehensive evaluation method to explore the rationality of the evaluation results.

Study area

Henan Province lies in mid-eastern China and at the middle-lower reaches of the Yellow River, with a boundary ranging between 31°23′ and 36°22′ N and 110°21′ and 116°39′ E (Figure 1). The mean annual precipitation is approximately from 500 to 900 mm, of which 50% falls during the monsoon seasons (June–September) (Chen et al., 2017). The total water resources of the province on average is 40 billion m3. The per-capita water resource availability of the province is 380–400 m3, falling far below the international standard for extreme water scarcity of 500 m3 (Li et al., 2021). According to the statistical data in 2018, the per-capita water resources in Henan Province was 311.6 m3, which was 1/6th of the national per-capita water resources, it is a serious water shortage area (Wang et al., 2022). Therefore, this paper chooses Henan Province of China as the research area and evaluates its water cycle health to provide some help to Henan Province's water resources management and sustainable economic and social development.
Fig. 1

The location of Henan Province.

Fig. 1

The location of Henan Province.

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Limited by the climate and geographical conditions, water resources in Henan Province have been determined, but the water consumption is increasinge year by year. According to the statistical data of Water Resources Bulletin of Henan Province, the changes of agricultural water, industrial water, domestic water, and ecological water consumption during the study period and the proportion of each water usage in the total water consumption are shown in Figures 2 and 3, respectively. From the perspective of the changing trend of water consumption, agricultural water consumption shows a fluctuating trend of growth, while industrial water consumption shows a relatively stable change, and there is not much increase or decrease. Domestic water consumption and ecological water consumption increase significantly with the increase ratio of 30.38 and 356.92%, respectively, which are closely related to the population change and ecological environment improvement. In terms of water usage proportion, the proportion of agricultural water is obviously higher than industrial, domestic, and ecological water consumption, which is related to Henan as a major agricultural province in China. Henan's agricultural, industrial, domestic, and ecological water consumption accounted for 51.11, 21.47, 17.35, and 10.07% in 2018. Except for agricultural water consumption, all remaining proportions are higher than those at the national average level (61.39, 20.97, 14.29, and 3.34%), which indicates that the water use structure of Henan Province needs to be improved. The raw data for indicator calculation are from official government reports and statistics (https://www.ha.stats.gov.cn/).
Fig. 2

Water consumption of Henan Province from 2007 to 2018.

Fig. 2

Water consumption of Henan Province from 2007 to 2018.

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Fig. 3

Water usage proportion of Henan Province from 2007 to 2018.

Fig. 3

Water usage proportion of Henan Province from 2007 to 2018.

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Evaluation index system

Construction of evaluation index system

The regional water cycle health emphasizes that urbanization development should not affect natural water cycle laws, and adequate water resources and efficient use patterns should reflect the social service function of water resources.

Therefore, the water cycle health evaluation system is constructed from four dimensions of water ecology, water quality, water abundance, and water utilization, as shown in Table 1.

Table 1

Index system of water cycle health in Henan Province

Target layerCriterion layerIndex layerDefinitionDescription
Index system for water cycle health in Henan Province A: Water ecology A1: Urban green coverage rate A1=Ga/Ta Ga – Green area; Ta – Total area; (Unit:%) 
A2: Variation of plain groundwater depth A2=Di+1-Di Di+1 – plain groundwater depth in i + 1 year;Di – plain groundwater depth in i year; (Unit: m) 
A3: Coefficienct of groundwater exploitation A3=Ex/Cex Ex – amount of groundwater mining; Cex – exploitable quantity of groundwater; (Unit: %) 
B: Water quality B1: Rate of standard river length B1=Std/S Std – the length of river whose water quality up to standard (grade III or better than grade III); S – total river length; (Unit: %) 
B2: Attainment rate of water function zone B2=num/Num num – the number of water function zone whose water quality up to standard (grade III or better than grade III); Num – total number of water function zone; (Unit: %) 
C: Water abundance C1: Per-capita water resource C1=Tol/Pou Tol – total amount of water resources; Pou – population; (Unit: m3/person) 
C2: Utilization rate of water resource C1=(Sur+Grd)/Tol Sur – Surface-water supply; Grd – ground water supply; (Unit: %) 
C3: Average water resource amount per ha C3=Tol/Area Area – irrigated area; (Unit: m3/ha) 
C4: Proportion of groundwater supply C4=Grd/Tsup Tsup – total amount of water supply; (Unit: %) 
D: Water utilization D1: Domestic water consumption rate D1=q1/q2 q1 – Consumption of domestic water; q2 – domestic water consumption; (Unit: %) 
D2: Agricultural water quota D2=Agi/Area Agi – Agricultural water consumption; (Unit: m3/ha) 
D3: Water consumption per unit of value added in industry D3=Iwc/Iav Iwc – water used by industry; Iav – unit value of the increase of industrial output value(10,000 yuan); (Unit: m3/10,000 yuan) 
D4: Sewage centralized treatment rate D4=Stc/Sq Stc – amount of disposaled sewage; Sq – total amount of sewage discharge; (Unit: %) 
D5: Proportion of reclaimed water utilization D5=q3/Q q3 – reclaimed water consumption; Q – total water consumption (Unit: %) 
Target layerCriterion layerIndex layerDefinitionDescription
Index system for water cycle health in Henan Province A: Water ecology A1: Urban green coverage rate A1=Ga/Ta Ga – Green area; Ta – Total area; (Unit:%) 
A2: Variation of plain groundwater depth A2=Di+1-Di Di+1 – plain groundwater depth in i + 1 year;Di – plain groundwater depth in i year; (Unit: m) 
A3: Coefficienct of groundwater exploitation A3=Ex/Cex Ex – amount of groundwater mining; Cex – exploitable quantity of groundwater; (Unit: %) 
B: Water quality B1: Rate of standard river length B1=Std/S Std – the length of river whose water quality up to standard (grade III or better than grade III); S – total river length; (Unit: %) 
B2: Attainment rate of water function zone B2=num/Num num – the number of water function zone whose water quality up to standard (grade III or better than grade III); Num – total number of water function zone; (Unit: %) 
C: Water abundance C1: Per-capita water resource C1=Tol/Pou Tol – total amount of water resources; Pou – population; (Unit: m3/person) 
C2: Utilization rate of water resource C1=(Sur+Grd)/Tol Sur – Surface-water supply; Grd – ground water supply; (Unit: %) 
C3: Average water resource amount per ha C3=Tol/Area Area – irrigated area; (Unit: m3/ha) 
C4: Proportion of groundwater supply C4=Grd/Tsup Tsup – total amount of water supply; (Unit: %) 
D: Water utilization D1: Domestic water consumption rate D1=q1/q2 q1 – Consumption of domestic water; q2 – domestic water consumption; (Unit: %) 
D2: Agricultural water quota D2=Agi/Area Agi – Agricultural water consumption; (Unit: m3/ha) 
D3: Water consumption per unit of value added in industry D3=Iwc/Iav Iwc – water used by industry; Iav – unit value of the increase of industrial output value(10,000 yuan); (Unit: m3/10,000 yuan) 
D4: Sewage centralized treatment rate D4=Stc/Sq Stc – amount of disposaled sewage; Sq – total amount of sewage discharge; (Unit: %) 
D5: Proportion of reclaimed water utilization D5=q3/Q q3 – reclaimed water consumption; Q – total water consumption (Unit: %) 

Determination of the index health threshold

The health standard is divided into five grades, namely, excellent, healthy, sub-healthy, unhealthy, and sick, corresponding to the health scores of 5, 5–4, 4–3, 3–2, and 2–1, respectively. Different regional development, ecological system evolution, and people's social expectations cause the different index thresholds of water cycle health. In this study, the index threshold in Henan Province is determined by the combination of national standards (GB 3838-2002, China) and historical references (Zhang et al., 2017) data along with the physical conditions of Henan Province (Table 2).

Table 2

Index threshold of water cycle health in Henan Province

IndexUnitExcellentHealthySub-healthyUnhealthySick
5(5,4](4,3](3,2](2,1]
A1 [100,50) (50,40] (40,30] (30,20] <20 
A2 [−2.0, − 1.5) (−1.5,0] (0,1.5] (1.5,2] (2,3] 
A3 [10,50) (50,70] (70,80] (80,90] (90,100] 
B1 [100,90) (90,70] (70,40] (40,30] (30,0] 
B2 [100,90) (90,60] (60,40] (40,20] (20,0] 
C1 m3 [900,500) [500,400) [400,200) [200,100) ≤ 100 
C2 [10,30) (30,50] (50,70] (70,90] (90,100] 
C3 m3/ha (0,100] (100,200] (200,250] (250,300] ≥ 300 
C4 (10,15] (15,25] (25,40] (40,60] >60 
D1 (20,40] (40,50] (50,60] (60,80] >80 
D2 m3/ha (100,300] (300,500] (500,800] (800,1,000] >1,000 
D3 m3/10,000 yuan (10,15] (15,25] (25,45] (45,60] >60 
D4 (0,0.2] (0.2,0.4] (0.4,0.6] (0.6,0.8] >0.8 
D5 (0,40] (40,60] (60,80] >80 
IndexUnitExcellentHealthySub-healthyUnhealthySick
5(5,4](4,3](3,2](2,1]
A1 [100,50) (50,40] (40,30] (30,20] <20 
A2 [−2.0, − 1.5) (−1.5,0] (0,1.5] (1.5,2] (2,3] 
A3 [10,50) (50,70] (70,80] (80,90] (90,100] 
B1 [100,90) (90,70] (70,40] (40,30] (30,0] 
B2 [100,90) (90,60] (60,40] (40,20] (20,0] 
C1 m3 [900,500) [500,400) [400,200) [200,100) ≤ 100 
C2 [10,30) (30,50] (50,70] (70,90] (90,100] 
C3 m3/ha (0,100] (100,200] (200,250] (250,300] ≥ 300 
C4 (10,15] (15,25] (25,40] (40,60] >60 
D1 (20,40] (40,50] (50,60] (60,80] >80 
D2 m3/ha (100,300] (300,500] (500,800] (800,1,000] >1,000 
D3 m3/10,000 yuan (10,15] (15,25] (25,45] (45,60] >60 
D4 (0,0.2] (0.2,0.4] (0.4,0.6] (0.6,0.8] >0.8 
D5 (0,40] (40,60] (60,80] >80 

Evaluation method

EFAST method

In the weight calculation, EFAST method reflects the global sensitivity by calculating the variance of model output caused by the coupling effect between single indicators and different indicators, and fully considers the influence of coupling effect between indicators on model results (Fang et al., 2019). Compared with the traditional weight calculation method, its combination with the model is more reasonable.

EFAST method was proposed by Saltelli et al. (1999). The method has the characteristics of stability, high efficiency, and fewer samples required. The variance that obtains from the coupling effect between indicators can be used to reflect the sensitivity of the indicator.

The total variance of the model output can be expressed as the sum of the variance of the coupling effect among the indicators:
formula
(1)
where V is variance; Vi is the variance of a single indicator xi; the variance of the interaction between multiple indicators is expressed by Vij, Vijk, V1,2…n.
The sensitivity index is defined as the ratio of the variance of each indicator and its coupling effect to the total variance. The direct contribution rate of single index xi to the total variance of model output is recorded as Mi, which is the first-order sensitivity index. The second order Mij, the third order Mijk and the higher order sensitivity index Mij…n can be obtained by coupling index x with other indexes. It is defined as follows:
formula
(2)
The sum of the above formula is the sensitivity of each index:
formula
(3)
The index sensitivity after coupling effects is used to calculate the weight, and the weight value Wi after normalization of the sensitivity Mmi of the i-th index can be obtained. The weight calculation formula can be expressed as:
formula
(4)

Cloud model theory

The traditional composite-index method does not consider uncertainty, and the fuzzy evaluation method only considers fuzziness. Compared with the above two methods, the cloud model not only reflects fuzziness in the evaluation results but it also involves randomness and fuzziness in the construction of the model before the evaluation results are calculated (Zhang et al., 2019). It can achieve the transformation between qualitative concepts and quantitative data, and the evaluation results are more in line with the objective reality. So the cloud model is the right choice for studying the two kinds of uncertainty.

The Cloud model theory proposed by academician Li Deyi uses expectation Ex, entropy En, and hyper-entropy He to deal with the uncertain problem between qualitative concept and quantitative description (Li et al., 1995).

In the Cloud model operation, we first need to generate two normal distribution random numbers, and then calculate the membership degree. The first normal distribution random number generated first , En is the expectation of random number, is the variance; and then generates the second normal distribution random number , in the same way, Ex is the expectation of random number, is the variance. The membership degree is obtained by . Cloud droplets are represented by coordinates , the above calculation process is repeated all the time, and the calculation is stopped when n cloud drops are generated (Gong, 2012).

In the cloud model, five indicator standard clouds are used to represent the five health grades, and the current status cloud represents the current value of the indicator. Ultimately, the membership degree of the current status cloud relative to the five indicator standard clouds is used to determine the grade distribution of the health status of the indicator. This study uses the MATLAB compiler to achieve a water cycle health evaluation based on the cloud model.

EFAST-Cloud model evaluation method

In this study, EFAST method is used to calculate the weight, combining with cloud model theory, as described below:

  • (1)
    Calculate the digital characteristics (Ex, En, He) of each evaluation index cloud model, the super-entropy He is an empirical constant. In order to reduce the influence of system randomness on the evaluation results, He is taken as 0.01. Ex is calculated by the following formula:
    formula
    (5)
    where Ex is expectation; Cmax is the maximum value of the grade threshold; Cmin is the minimum value of the grade threshold.

According to the nature of cloud, when the evaluation grade is close to the threshold, it is the transition from one grade to another, so it has uncertainty and should belong to two grades at the same time (Deng et al., 2021):
formula
(6)
Namely,
formula
(7)
  • (2)
    The positive cloud generator is used to calculate the membership degree of each index corresponding to different grades. With the Python language, the membership degree is calculated 1,000 times, and the final membership degree is obtained by calculating the membership degree. The formula is as follows:
    formula
    (8)
    where μ is the membership degree; x is the index value; Ex is the same as above.
  • (3)

    According to the index data, the EFAST method is used to calculate the weight Wi of each index by the Simlab software.

In order to avoid the impact of positive and negative indicators on the evaluation of water resources carrying capacity, the deviation standardization method is adopted to conduct dimensionless treatment to make it between 0 and 1 (Ren et al., 2020), then Cmin = 0, Cmax = 1.

Positive indicators:
formula
(9)
Negative indicators:
formula
(10)
where xij is the j index value of the year i, , are the minimum and maximum values of this index.

According to the nature of cloud model, Ex = 0.5, Simlab software is used to calculate the membership degree. The set sampling times of EFAST method are 65 times greater than the number of parameters, and the obtained analysis results are valid. In this paper, the number of samples is set to 12,000.

  • (4)
    Calculate the comprehensive membership degree. According to the principle of maximum membership in the Cloud model, the evaluation level is determined:
    formula
    (11)
    where μmi and Cmi represent the membership degree and comprehensive membership degree, respectively, when the i-th index evaluation grade is m.

Index weight

Index sensitivity analysis scheme

Simlab is used to calculate the global sensitivity analysis. The specific scheme is divided into the following four steps.

Firstly, the value range and distribution of model input parameters are defined in Simlab, and it is considered that the parameters are evenly distributed in the value range.

Secondly, random sampling is used to generate the multi-dimensional parameter set. The number of sampling times should be more than 65 times the number of parameters, and the number of sampling times is set to 12,000.

Thirdly, we set the generated parameter into the corresponding evaluation model file, run the model and then sort out the simulating results.

Finally, the data generated by the simulation is sorted into a text format that can be recognized by Simlab, and the final sensitivity analysis results are obtained through the Simlab analysis.

Analysis of index sensitivity calculation results

According to EFAST method, the sensitivity of indicators is calculated as shown in Figure 4. Mi is the first-order sensitivity index, Mmi-Mi is the high-order sensitivity index due to the coupling effect between indexes.
Fig. 4

Sensitivity index of each indicator.

Fig. 4

Sensitivity index of each indicator.

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It can be seen from Figure 4 that high-order sensitivity accounts for a certain proportion in the global sensitivity calculation, that is to say, the influence of the coupling effect between indicators on weight calculation cannot be ignored. The high-order sensitivity index (Mmi-Mi) accounts for about 13% of the global sensitivity index. The Mi values range from 0.042 to 0.387, and the MmiMi values range from 0.01 to 0.038. Meanwhile, the higher the first sensitivity index is, the higher the high sensitivity index is. The more sensitive index will be given more weight. EFAST method reflects the global sensitivity by calculating the variance of model output caused by a single index and the coupling effect between different indexes, and it adequately considers the influence of coupling effect between indexes on model results.

Results of weighting calculation

EFAST method is used to calculate the weights of 14 indicators in four dimensions of water ecology, water quality, water abundance, and water utilization. The results are shown in Table 3.

Table 3

Results of weighting calculation based on the EFAST method

Target layerCriterion layerCriterion layer weightIndex layerIndex layer weight
Index system for water cycle health in Henan province A: Water ecology 0.1718 A1: Urban green coverage rate 0.0782 
A2: Variation of plain groundwater depth 0.0679 
A3: Coefficienct of groundwater exploitation 0.0256 
B: Water quality 0.3600 B1: Rate of standard river length 0.1652 
B2: Attainment rate of water function zone 0.1948 
C: Water abundance 0.2169 C1: Per-capita water resource 0.0244 
C2: Utilization rate of water resource 0.0383 
C3: Average water resource amount per ha 0.0299 
C4: Proportion of groundwater supply 0.1244 
D: Water utilization 0.2513 D1: Domestic water consumption rate 0.0504 
D2: Agricultural water quota 0.0495 
D3: Water consumption per unit of value added in industry 0.0374 
D4: Sewage centralized treatment rate 0.0839 
D5: Proportion of reclaimed water utilization 0.0302 
Target layerCriterion layerCriterion layer weightIndex layerIndex layer weight
Index system for water cycle health in Henan province A: Water ecology 0.1718 A1: Urban green coverage rate 0.0782 
A2: Variation of plain groundwater depth 0.0679 
A3: Coefficienct of groundwater exploitation 0.0256 
B: Water quality 0.3600 B1: Rate of standard river length 0.1652 
B2: Attainment rate of water function zone 0.1948 
C: Water abundance 0.2169 C1: Per-capita water resource 0.0244 
C2: Utilization rate of water resource 0.0383 
C3: Average water resource amount per ha 0.0299 
C4: Proportion of groundwater supply 0.1244 
D: Water utilization 0.2513 D1: Domestic water consumption rate 0.0504 
D2: Agricultural water quota 0.0495 
D3: Water consumption per unit of value added in industry 0.0374 
D4: Sewage centralized treatment rate 0.0839 
D5: Proportion of reclaimed water utilization 0.0302 

From Table 3, it can be seen that in the four criterion layers, the dimension of water quality has the largest weight, which is more than twice as heavy as the dimension water ecology with the lowest weight. Among the 14 indicators, the attainment rate of water function zone, rate of standard river length and proportion of groundwater supply are the indicators with larger weight, while the indicators with smaller weight are per-capita water resource, coefficient of groundwater exploitation, and average water resource amount per ha.

Determination of the cloud digital feature

According to the properties of cloud model and formulas (5) and (7), three cloud digital features (Ex, En, He) of five health degrees (Excellent–Healthy–Sub-healthy–Unhealthy-Sick) corresponding to 14 evaluation indexes in Henan province were calculated. According to the positive Cloud generator, the Cloud model maps corresponding to the four different dimensions of evaluating indexes are drawn, namely water ecology, water quality, water abundance, and water utilization. The cloud charts of three evaluation indexes of water ecology, two evaluation indexes of water quality, four evaluation indexes of water abundance, and five evaluation indexes of water utilization are shown in Figures 58, respectively. The abscissa of the cloud chart represents the index value, and the ordinate represents the membership degree of each bearing grade.
Fig. 5

Cloud chart of dimension index of water ecology.

Fig. 5

Cloud chart of dimension index of water ecology.

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Fig. 6

Cloud chart of dimension index of water quality.

Fig. 6

Cloud chart of dimension index of water quality.

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Fig. 7

Cloud chart of dimension index of water abundance.

Fig. 7

Cloud chart of dimension index of water abundance.

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Fig. 8

Cloud chart of dimension index of water utilization.

Fig. 8

Cloud chart of dimension index of water utilization.

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Cloud model can show the uncertainty and fuzziness of qualitative concepts, and can effectively avoid the absoluteness of evaluation. It can be seen from the cloud chart that the cloud chart of each grade of the index has crossed parts. For example, when the green coverage rate of the built-up area is 39.4%, the membership degree of the index is 0.344, 0.670, 0.005, 0, and 0.040, respectively. It means that the probability of the green coverage rate of the built-up area is 0.344 for the very healthy grade, 0.670 for the healthy grade, 0.005 for the sub-healthy grade, and 0.040 for the unhealthy grade. The healthy grade has the largest probability.

Analysis of evaluation results

The index layer

The healthy status of each evaluation index in Henan Province over the years is drawn by the color scale, and Figure 9 is obtained, in which crimson represents sick, red represents unhealthy, orange represents sub-healthy, yellow represents healthy, and green represents very healthy.
Fig. 9

Color scale of healthy status of indicators in Henan Province from 2007 to 2018.

Fig. 9

Color scale of healthy status of indicators in Henan Province from 2007 to 2018.

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As can be seen from Figure 9, the index of urban green coverage rate (A1) is the best. Except for sub-health in 2007, it was at the level of health or above it, and it was very healthy in 2018. The average water resource amount per ha (C3) of the index was the worst, except for the unhealthy performance in 2010 and 2017, all of which showed their pathosis. The centralized sewage treatment rate of the indicator (D4) increased dramatically, which reached a very healthy state from an unhealthy one from 2007 to 2016, and it lasted until 2018. Overall, the situation of 14 indicators between 2007 and 2018 has significantly improved, but there is still a certain gap from the excellent state.

The criterion layer

According to the statistical index data from 2007 to 2018, the dimension grade evaluation results of water cycle health in Henan Province are shown in Figures 10 and 11. In Figure 10, 5 represents excellent, 4 represents healthy, 3 represents sub-healthy, 2 represents unhealthy, and 1 represents sick. Figure 11 shows the proportion of membership degrees of different grades in each criterion layer.
Fig. 10

Health status of each criterion layer in Henan Province from 2007 to 2018.

Fig. 10

Health status of each criterion layer in Henan Province from 2007 to 2018.

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Fig. 11

Proportion of membership degree of each health level in Henan Province from 2007 to 2018.

Fig. 11

Proportion of membership degree of each health level in Henan Province from 2007 to 2018.

Close modal

It can be seen from Figure 10 that in terms of health level, among the four criteria, the criterion of water ecology is the best, and it has remained healthy for many years. The criterion layer of water abundance is poor, and it is unhealthy or below in nine years. The level of water quality criteria has fluctuated a lot over the years, and tends to be better year by year. Except for 2007, the water utilization criteria is above sub-healthy, and reaches excellent in 2017 with a good overall situation.

It can be seen from Figure 11 that the criterion layer of water abundance fluctuates greatly for many years. Generally speaking, it is the worst criterion layer with the highest proportion of unhealthy and sick grades. In nine of the 12 years, these two levels account for more than 70%, and the average proportion for many years is as high as 74.4%, which is in line with the water resources shortage in Henan Province.

The water ecology criterion layer has the best evaluation situation, and the evaluation grade reached healthy or above accounting for 69.4% for many years. In seven of the 12 years, the proportion of healthy and excellent are more than 70%, with a peak of 86.2% in 2018. Over the years, the water ecology has been steadily improved year by year, which is closely related to the efforts of Henan Province to strengthen the construction of water ecological civilization.

Water quality and water utilization are in the middle level. However, they all show an obvious trend of improvement year by year, and the health grade is steadily improved. In the dimension of water resource quality, the proportion of unhealthy or below level has been less than 1% since 2014. At this point, this criterion layer performs best.

The target layer

According to the statistical index data from 2007 to 2018, EFAST-Cloud model was adopted to evaluate the water cycle health of Henan Province. The calculated proportion of the membership degree of each health grade is shown in Figure 12. The final water cycle health grade of different years is shown in Table 4.
Table 4

Health grade of water cycle in Henan Province from 2007 to 2018

Year200720082009201020112012
Grade Sub-healthy Sub-healthy Sub-healthy Unhealthy Sub-healthy Unhealthy 
Year 2013 2014 2015 2016 2017 2018 
Grade Sub-healthy Sub-healthy Healthy Healthy Healthy Healthy 
Year200720082009201020112012
Grade Sub-healthy Sub-healthy Sub-healthy Unhealthy Sub-healthy Unhealthy 
Year 2013 2014 2015 2016 2017 2018 
Grade Sub-healthy Sub-healthy Healthy Healthy Healthy Healthy 
Fig. 12

The proportion of health grade membership in target layer in Henan Province from 2007 to 2018.

Fig. 12

The proportion of health grade membership in target layer in Henan Province from 2007 to 2018.

Close modal

As can be seen from Figure 12, the overall health status of water cycle in Henan Province from 2007 to 2018 improved gradually, and the average distribution of water cycle health grade for many years is 4.6–30.3–31.4–22.5–11.2% (Excellent-Healthy-Sub-healthy-Unhealthy-Sick). Among them, the proportion of sub-healthy grades is the largest, which indicates that the whole region has been in a sub-healthy state for many years. Since 2015, the proportion of healthy or above has exceeded 40%, and reached 67% in 2018, which is in the best state. From 2018 to 2012, the proportion of unhealthy or below grade was over 40% with the poor state. It dropped to below 20% in 2016, indicating that the health situation of water cycle in Henan Province has been improved effectively.

As can be seen from Table 3, except for 2010 and 2012, the level of water cycle health was unhealthy, and the other years were all sub-healthy or above. Especially since 2015, it has been in a healthy state for several consecutive years, which is partly attributed to the implementation of the most stringent water resources management system in 2013 and the official operation of the middle route of the South-to-North Water Diversion Project at the end of 2014, which is inseparable with the strengthening of water ecological civilization construction in Henan Province.

Comparison between EFAST-cloud model and traditional evaluation methods

The water cycle is an important factor to maintain the sustainable development of human beings (Zhang et al., 2021). Objectively evaluating the health status of the water cycle is conducive to formulating scientific and reasonable water resources management measures. However, due to the fact that the health status of water cycle is affected by many factors, the research is often disturbed by uncertainty and fuzziness, in the evaluation of water cycle health, the sensitivity of the evaluation index and the fuzziness of the threshold value need special attention.

As an uncertain model of qualitative and quantitative transformation, cloud model can fully reflect the randomness and fuzziness of linguistic concepts, and it can effectively transform between quantitative description and qualitative concepts. The Cloud model obtains the actual cloud chart of the object to be studied by processing the data through Matlab software, and draws the conclusion through intuitive analysis of the cloud chart. Therefore, the uncertain problem of water cycle health grade evaluation can be properly solved, and it has a unique advantage in dealing with randomness and fuzziness. Moreover, compared with the traditional evaluation method, the weight of each index based on cloud model generation is largely from the objective data, which can reduce the influence of subjective factors on the evaluation results as less as possible.

In order to highlight the sensitivity of the evaluation index, different from the traditional entropy weight method, this study combines EFAST method with cloud model theory to calculate the weight of the index. The proportions of each index of the two methods are shown in Figure 13, and the comparison of calculation results of the two methods is shown in Figure 14.
Fig. 13

The weight proportion of each index in the entropy weight method and EFAST-Cloud model method.

Fig. 13

The weight proportion of each index in the entropy weight method and EFAST-Cloud model method.

Close modal
Fig. 14

Comparison between weights calculated by EFAST method and entropy weight method.

Fig. 14

Comparison between weights calculated by EFAST method and entropy weight method.

Close modal

As can be seen from Figure 14, compared with the traditional weight calculation method, the weight distribution of indexes calculated by the entropy weight method is comparatively even, and the weight values are concentrated between 0.02 and 0.112. The EFAST method takes into account the coupling effect between the indexes and the sensitivity of each index to the model. The weight value ranges from 0.024 to 0.194, which could more accurately reflect the importance of each index.

Based on the weight calculation method, this study innovatively constructs EFAST-Cloud model method and applies it to the regional water cycle health evaluation. EFAST method can consider the coupling effect between indexes when calculating the weight. The fuzziness of grading threshold can be considered when the cloud model is used to calculate the grade of water cycle health. EFAST-Cloud model approach can take into account the sensitivity of indicators to the evaluation model. The improved comprehensive evaluation method of water cycle health not only ensures the calculation accuracy, but also makes the evaluation performance more and more optimized and the calculation more and more simple.

The results of EFAST-Cloud model are compared with those of fuzzy comprehensive evaluation. The results are shown in Figure 15. Among them, 5 represents excellent, 4 for healthy, 3 for sub-healthy, 2 for unhealthy, and 1 for sick.
Fig. 15

Comparison between fuzzy comprehensive evaluation and the EFAST-Cloud model method.

Fig. 15

Comparison between fuzzy comprehensive evaluation and the EFAST-Cloud model method.

Close modal

It can be seen from Figure 13 that in the 12 years from 2007 to 2018, only in 2009, the results of the two evaluation methods are different (only one grade differs), and the results of other years are completely consistent. Therefore, it is feasible and reasonable to use EFAST-Cloud model in water cycle health assessment.

Although the Cloud model can effectively avoid the absoluteness of evaluation, the membership degree of the two grades may be completely consistent when determining the final bearing grade. In order to avoid this phenomenon, the weighted average value can be used to determine the final evaluation grade instead of the maximum membership degree principle in the future.

Measures and suggestions

Although the health status of water cycle in Henan province has been healthy since 2015, there is still a big gap between the state of water cycle in Henan Province and that of excellent, especially in the dimension of water abundance, Henan Province is still at a poor level. The situation of average water resource amount per ha and proportion of groundwater supply is not optimistic. This is related to the strategic position of Henan Province as a largely agricultural province and the present situation of over exploitation of groundwater. In the prospective improvement of water cycle, it is urgent for Henan Province to adopt advanced irrigation technology, to strengthen the utilization of water resources and strengthen the restoration of water environment, to reuse rationally reclaimed water, and to reduce the exploitation of groundwater.

Besides, it is necessary to form an efficient and economical multi-source water-supply mode. Inter-basin water transfer is a feasible means to solve the problem of uneven distribution of water resources among regions, and the planning-year analysis shows the effects of the water transfer project. However, the region's development should be compatible with the carrying capacity of water resources. If the population does not match regional water resources, industry restructuring and population decreases must ensure to achieve a healthy water cycle status. This also applies to current reform measures. The government could move manufacturing, logistics, wholesale markets and some public service functions away from the main urban areas. Such measures will somewhat ease population growth, reduce water consumption, and alleviate pollution of water function areas, thereby improving the health status of the regional water cycle.

In this study, a water cycle health evaluation method based on EFAST-cloud model is proposed, and Henan Province is selected as a typical study area to evaluate its water cycle health status. A total of 18 indicators including water ecology, water quality, water abundance, and water utilization are constructed. The study analyzes the sensitivity of indicators by using Simlab software, and then calculates the weight of indicators by using EFAST method. The results show that the quality of water resources is the dimension with the largest weight, and the compliance rate of water function area is the index with the largest weight. Different from the traditional weight calculation method, EFAST method can get a large difference between the weight indexes, which can better reflect the coupling effect between the indexes and the sensitivity of each index to the model.

After getting the weight of each index by EFAST method, the water cycle health status of Henan Province was evaluated by using cloud model according to the statistical data from 2007 to 2018. The results show that the average distribution of health levels is 4.6–30.3–31.4–22.5–11.2% (Excellent – Healthy – Sub-healthy – Unhealthy – Sick). The Sub-healthy level accounted for the largest proportion, indicating that the region was in a Sub-healthy state for many years. On the whole, the health status of water cycle in Henan Province shows a trend of increasing year by year. Except for 2010 and 2012, the other years are Sub-healthy and above. In 2015, the health status reached the year of status quo.

The evaluation results are compared with those of fuzzy comprehensive evaluation. The results show that the evaluation results of EFAST-Cloud model are consistent with those of fuzzy comprehensive evaluation, and the results can be relied on. However, as an uncertain model of qualitative and quantitative transformation, it can better reflect the randomness and fuzziness of language concepts. It can also effectively transform qualitative evaluation indicators into objective and accurate evaluation values, which is suitable for scientific evaluation and analysis of different indicators. Hence it has unique advantages in dealing with the problems of randomness and fuzziness.

The project is financially supported by the National Natural Science Foundation of the People's Republic of China (51879106), the Central Plains Science and technology innovation leader Project (214200510001) and the Science and Technology Innovation Team in Universities of Henan Province (20IRTSTHN010). In addition, the authors would like to express their sincere gratitude to the anonymous reviewers for their constructive comments and useful suggestions that helped us improve this study.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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