Dynamic evaluation of regional water resources carrying capacity based on set pair analysis and partial connection number

Abstract

In order to describe the micro motion between the connection number components and seek a more applicable evaluation model, quantitatively evaluate and analyze regional water resources carrying capacity (WRCC). Firstly, an evaluation index system and grade standards of regional WRCC were constructed. Then, a method for determining the connection number was proposed, which considered the micro motion between the connection number components in system structure. Finally, built an evaluation model based on set pair analysis (SPA) and partial connection number (PCN) that used subtraction set pair potential (SPP) to identify vulnerability factors, and identification results were compared with total partial connection number (TPCN). The model was applied to Huaibei City, Anhui Province, China. The results showed that: the WRCC grade value was between 2 and 3 that was poor; the support and regulation subsystem grade value was between 2 and 3, and the pressure subsystem grade value was between 1 and 2. SPP identified that the support force and regulation force subsystem were the vulnerable subsystems. Eight indexes such as water resources per capita, rate of ecological water consumption and density of population were the main indicators causing the poor WRCC, which were in good agreement with the local measured data. In addition, the SPP and TPCN are compared to further verify rationality of the connection number determination method and reliability of the identification results. The model established in this paper has strong applicability and can also be used for the dynamic evaluation of other resources, environment and ecological carrying capacity. The results in this study can provide a scientific basis for water resources management and decision-making.

Quantitative Evaluation and Diagnosis of Water Resources Carrying Capacity (WRCC) Based on Dynamic Difference Degree Coefficient in the Yellow River Irrigation District

“…. , n k }, where x ikj denotes the value of index j in subsystem k for sample i, m denotes the number of evaluation samples, k = 1, 2, 3 denotes the water resources carrying support force subsystem, carrying pressure force subsystem, and carrying regulation force subsystem, respectively, and n k denotes the number of indexes in subsystem k. In addition, in this study, the WRCC in the Yellow River irrigation district was divided into three evaluation grades (Cui et al, 2018;Li et al, 2021) {s gkj |g = 1, 2, 3; k = 1, 2, 3; j = 1, 2, . .…”

“…They were inverse potential s 2 (u ikj )∈[-1.0, -0.6), partial inverse potential s 2 (u ikj )∈[-0.6, -0.2), symmetrical potential s 2 (u ikj )∈[-0.2, 0.2], partial identical potential s 2 (u ikj )∈(0.2, 0.6], and identical potential s 2 (u ikj )∈(0.6, 1.0], respectively. Furthermore, the index of partial inverse potential or inverse potential was diagnosed as the main obstacle factor hindering the improvement of WRCC and also the important object of water resources management in the Yellow River irrigation district (Li et al, 2021).…”

“…Wan et al (2021) built a river health evaluation and prediction model based on SPA and extension theory. Nevertheless, the majority of studies only reflect the evaluation results based on the connection number components (Li et al, 2021). Thus, it is difficult to obtain the value of the complete connection number, which results in a loss of information, thereby limiting the development of SPA.…”

“…Tang (2009) proposed an expert estimation method of the difference degree coefficient. Furthermore, Pan et al (2016) and Li et al (2021) respectively constructed the trapezoidal and triangular fuzzy numbers of the difference degree coefficient, and determined the variation range of difference degree coefficient at a given cut set level. Jin et al (2019aJin et al ( , 2019b allocated the difference degree coefficient in proportions using grey correlation degree and full partial certainty, respectively.…”

“…Jin et al (2019aJin et al ( , 2019b allocated the difference degree coefficient in proportions using grey correlation degree and full partial certainty, respectively. However, the majority of these methods are relatively rough and only obtain an approximate value or an interval range of difference degree coefficients (Tang, 2009;Pan et al, 2016;Li et al, 2021). In fact, accurately calculating the value of the difference degree coefficient remains difficult, which leads to large deviations between the research results and reality.…”