Research on water resources carrying capacity evaluation based on innovative RCC method
Scientific assessment of water resources carrying capacity (WRCC) is the basis for implementing water resources protection measures. To make the evaluation more objective and reasonable, this study created a WRCC evaluation method based on R-clustering-variance coefficient method coupled with cloud...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X22003478 |
| _version_ | 1811256585699196928 |
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| author | Tianzi Wang Shengqi Jian Jiayi Wang Denghua Yan |
| author_facet | Tianzi Wang Shengqi Jian Jiayi Wang Denghua Yan |
| author_sort | Tianzi Wang |
| collection | DOAJ |
| description | Scientific assessment of water resources carrying capacity (WRCC) is the basis for implementing water resources protection measures. To make the evaluation more objective and reasonable, this study created a WRCC evaluation method based on R-clustering-variance coefficient method coupled with cloud model (RCC) for the state study of WRCC in a specific region. The study collected and summarized the available indicators through the review of existing studies to build a database of extensively selected indicators, and also combined with the characteristics of the study area to construct the initial indicators database. Then the indicators were categorized into 12 factor layers under the concept of water resources carrying subjects and objects, and the R clustering-variance coefficient method was introduced to screen the indicators to construct a concise and effective evaluation index system. Finally, combined with the cloud model to identify the comprehensive state of WRCC, the quantitative expression of WRCC was realized. Taking Henan Province of China as the research area, a total of 81 available indicators were collected, and an evaluation index system composed of 30 indicators was constructed after screening. During the evaluation years of 2005–2017, the WRCC level in Henan Province gradually raised from level IV (overload) to level II (weakly bearable), and the results of the study were stable and consistent with the changing trend of existing studies. The study can provide new ideas for the evaluation indicators selection and quantitative evaluation of water resources carrying capacity or other carrying capacity for a specific county, city, or provincial study area. |
| first_indexed | 2024-04-12T17:42:30Z |
| format | Article |
| id | doaj.art-987a47c268ec4b4494c0b2ad25843d1a |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-12T17:42:30Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-987a47c268ec4b4494c0b2ad25843d1a2022-12-22T03:22:45ZengElsevierEcological Indicators1470-160X2022-06-01139108876Research on water resources carrying capacity evaluation based on innovative RCC methodTianzi Wang0Shengqi Jian1Jiayi Wang2Denghua Yan3College of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou 450003, ChinaCollege of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou 450003, China; Corresponding author.Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission, Zhengzhou 450003, ChinaCollege of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou 450003, ChinaScientific assessment of water resources carrying capacity (WRCC) is the basis for implementing water resources protection measures. To make the evaluation more objective and reasonable, this study created a WRCC evaluation method based on R-clustering-variance coefficient method coupled with cloud model (RCC) for the state study of WRCC in a specific region. The study collected and summarized the available indicators through the review of existing studies to build a database of extensively selected indicators, and also combined with the characteristics of the study area to construct the initial indicators database. Then the indicators were categorized into 12 factor layers under the concept of water resources carrying subjects and objects, and the R clustering-variance coefficient method was introduced to screen the indicators to construct a concise and effective evaluation index system. Finally, combined with the cloud model to identify the comprehensive state of WRCC, the quantitative expression of WRCC was realized. Taking Henan Province of China as the research area, a total of 81 available indicators were collected, and an evaluation index system composed of 30 indicators was constructed after screening. During the evaluation years of 2005–2017, the WRCC level in Henan Province gradually raised from level IV (overload) to level II (weakly bearable), and the results of the study were stable and consistent with the changing trend of existing studies. The study can provide new ideas for the evaluation indicators selection and quantitative evaluation of water resources carrying capacity or other carrying capacity for a specific county, city, or provincial study area.http://www.sciencedirect.com/science/article/pii/S1470160X22003478WRCCR clusteringCloud modelHenan, China |
| spellingShingle | Tianzi Wang Shengqi Jian Jiayi Wang Denghua Yan Research on water resources carrying capacity evaluation based on innovative RCC method Ecological Indicators WRCC R clustering Cloud model Henan, China |
| title | Research on water resources carrying capacity evaluation based on innovative RCC method |
| title_full | Research on water resources carrying capacity evaluation based on innovative RCC method |
| title_fullStr | Research on water resources carrying capacity evaluation based on innovative RCC method |
| title_full_unstemmed | Research on water resources carrying capacity evaluation based on innovative RCC method |
| title_short | Research on water resources carrying capacity evaluation based on innovative RCC method |
| title_sort | research on water resources carrying capacity evaluation based on innovative rcc method |
| topic | WRCC R clustering Cloud model Henan, China |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X22003478 |
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