Evaluation of water cycle health status based on a cloud model

The water cycle is important in maintaining sustainable development in the Anthropocene. Objective evaluation of the water cycle health status is conducive to the development of scientific and rational water management measures. The particularity of evaluation of the water cycle means the random error of the evaluation index and the threshold fuzziness of the evaluation degree are issues that require attention. This study considered 13 cities in the Beijing-Tianjin-Hebei region to establish a method based on a cloud model for the assessment of water cycle health. The water cycle health status was evaluated in terms of four dimensions: water ecology, water quality, water quantity, and water use. Moreover, the evaluation result was compared with results derived using a composite index method. The amount of change in the evaluation results under 11 random error conditions and the maximum fuzzy conditions of each threshold grade were simulated. It was found that the established cloud model produced results consistent with the composite index method, while it had 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 statistical data from 2000 to 2014, the average distribution of water cycle health grade in the Beijing-Tianjin-Hebei region was 28%-16%-34%-13%-9% (sick-unhealthy-subhealthy -healthy-excellent), i.e., the region was generally in a subhealthy state, in which the water quantity dimension was the worst and the water use dimension was the best. The degree of influence of the random error of the index on the evaluation results of each layer increased with the increase of the random error, and the threshold boundary fuzziness of the indicator grade led to a trend of homogenization of each grade component. The control of the random errors of the indicators under different sensitivity ranges could effectively improve the rationality and stability of the evaluation results. The research results presented in this paper could provide technical support for water cycle evaluation, measurement of indicator data, and statistical error control. (C) 2019 Elsevier Ltd. All rights reserved.