Abstract
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.
HIGHLIGHTS
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
INTRODUCTION
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.
MATERIALS AND METHODS
Study area
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.
Index system of water cycle health in Henan Province
Target layer . | Criterion layer . | Index layer . | Definition . | Description . |
---|---|---|---|---|
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 layer . | Criterion layer . | Index layer . | Definition . | Description . |
---|---|---|---|---|
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).
Index threshold of water cycle health in Henan Province
Index . | Unit . | Excellent . | Healthy . | Sub-healthy . | Unhealthy . | Sick . |
---|---|---|---|---|---|---|
5 . | (5,4] . | (4,3] . | (3,2] . | (2,1] . | ||
A1 | % | [100,50) | (50,40] | (40,30] | (30,20] | <20 |
A2 | m | [−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 | (0,40] | (40,60] | (60,80] | >80 |
Index . | Unit . | Excellent . | Healthy . | Sub-healthy . | Unhealthy . | Sick . |
---|---|---|---|---|---|---|
5 . | (5,4] . | (4,3] . | (3,2] . | (2,1] . | ||
A1 | % | [100,50) | (50,40] | (40,30] | (30,20] | <20 |
A2 | m | [−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 | (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.
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:where Ex is expectation; Cmax is the maximum value of the grade threshold; Cmin is the minimum value of the grade threshold.
- (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: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.
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)
EVALUATION OF WATER CYCLE HEALTH IN HENAN PROVINCE BASED ON EFAST-CLOUD MODEL
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
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 Mmi–Mi 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.
Results of weighting calculation based on the EFAST method
Target layer . | Criterion layer . | Criterion layer weight . | Index layer . | Index 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 layer . | Criterion layer . | Criterion layer weight . | Index layer . | Index 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
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
Color scale of healthy status of indicators in Henan Province from 2007 to 2018.
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
Health status of each criterion layer in Henan Province from 2007 to 2018.
Proportion of membership degree of each health level in Henan Province from 2007 to 2018.
Proportion of membership degree of each health level in Henan Province from 2007 to 2018.
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
Health grade of water cycle in Henan Province from 2007 to 2018
Year . | 2007 . | 2008 . | 2009 . | 2010 . | 2011 . | 2012 . |
---|---|---|---|---|---|---|
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 |
Year . | 2007 . | 2008 . | 2009 . | 2010 . | 2011 . | 2012 . |
---|---|---|---|---|---|---|
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 |
The proportion of health grade membership in target layer in Henan Province from 2007 to 2018.
The proportion of health grade membership in target layer in Henan Province from 2007 to 2018.
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.
DISCUSSION
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.
The weight proportion of each index in the entropy weight method and EFAST-Cloud model method.
The weight proportion of each index in the entropy weight method and EFAST-Cloud model method.
Comparison between weights calculated by EFAST method and entropy weight method.
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.
Comparison between fuzzy comprehensive evaluation and the EFAST-Cloud model method.
Comparison between fuzzy comprehensive evaluation and the EFAST-Cloud model method.
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.
CONCLUSION
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.
ACKNOWLEDGEMENTS
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 AVAILABILITY STATEMENT
Data cannot be made publicly available; readers should contact the corresponding author for details.
CONFLICT OF INTEREST
The authors declare there is no conflict.