| Evaluation and prediction of water resources carrying capacity in 31 provinces, municipalities and autonomous regions of China |
|
View Fulltext View/Add Comment Download reader |
| |
| DOI：10.7606/j.issn.1000-7601.2023.04.24 |
| Key Words: water resources carrying capacity TOPSIS model obstacle factors BP neural network 31 provinces, municipalites and autonomous regions of China |
| Author Name | Affiliation | | LIU Huan | State Key Laboratory of Eco\|hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an, Shaanxi 710048, China | | SONG Xiaoyu | State Key Laboratory of Eco\|hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an, Shaanxi 710048, China | | LI Lei | State Key Laboratory of Eco\|hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an, Shaanxi 710048, China | | CHAO Zhilong | Shaanxi Hydrology and Water Resources Survey Bureau, Xi’an, Shaanxi 710068, China |
|
| Hits: 732 |
| Download times: 556 |
| Abstract: |
| To evaluate the carrying capacity of provincial water resources in China, an index system was established based on four subsystems of water resources\|society\|economy\|ecological environment. The TOPSIS model weighted by the combination of AHP and entropy method was used to comprehensively evaluate water resources carrying capacity of 31 provinces, municipalities and autonomous regions in China from 2010 to 2019, The model’s temporal and spatial variation characteristics were analyzed combining with the M-K trend method. The obstacle factor diagnosis was carried out on the evaluation results. On this basis, BP neural network was used to predict the water resources carrying capacity of each province (municipality, autonomous region) from 2020 to 2025. The results showed that: (1) The water resources carrying capacity in 31 provinces, municipalities and autonomous regions of China showed a fluctuating upward trend and the overall state was in a critical carrying state. (2) The water resources carrying capacity in the country had significant regional differentiation pattern, with better carrying capacity in Southwest, South China, and Northeast China, and the weakest in North China and Northwest China. (3) During the study period, the water resources carrying capacity deteriorated in Northwest and Northeast regions, and some areas in South China and North China also deteriorated, while most areas in East China, Central China and Southwest China improved. (4) Water production modulus, consumption of agricultural chemical fertilizer, water resources per capita, water supply modulus, comprehensive water consumption per capita, total wastewater discharge, water resources development and utilization ratio were seven obstacle factors that had the strongest influence on water resources carrying capacity in China, and the influence intensity decreased in turn. (5) In the future, the water resources carrying capacity will be gradually improved in most parts of the country, Shandong, Henan and Liaoning may form severe deterioration areas. |
|
|
|