The growing water crisis in Central Asia and the driving forces behind it

The growing water crisis in Central Asia and the driving forces behind it

Publication Year:
2022
Authors:
Wang, Xuanxuan; Chen, Yaning; Fang, Gonghuan; Li, Zhi; Liu, Yongchang
Language:
English
Resource Type:
Journal Article
This study assess the water crisis from multiple perspectives using the water stress index (WSI), safe drinking water and water pollution indicators, and quantitatively analyze the impact of climate change, population growth, poverty, urbanization and transboundary river management on the water crisis.
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Resource Information

Abstract

Central Asia (CA) is one of the most severe water crisis areas on earth, which has seriously limited the achievement of sustainable development goals (SDGs) in the region. However, a multi-perspective analysis on the process and driving factors of the water crisis in CA has not been conducted. Therefore, we assess the water crisis from multiple perspectives using the water stress index (WSI), safe drinking water and water pollution indicators, and quantitatively analyze the impact of climate change, population growth, poverty, urbanization and transboundary river management on the water crisis. Results show that the water crisis in CA is intensifying. Uzbekistan and Turkmenistan belong to the “severe water stress” category, and the WSIs are increasing in both countries. Tajikistan is classified as “high water stress”. Kyrgyzstan and Kazakhstan both exhibit “moderate water stress”. Moreover, the proportion of the rural population with access to safe drinking water is significantly lower than that of the urban population in all the CA countries. The impact of human activities on water crisis in CA is more significant than that of climatic factors. Both cultivated land area and population are significant factors affecting the water crisis in CA (p < 0.05), with the regression coefficients of 0.62 and 1.62, respectively. Our research provides an essential reference for the sustainable management of water resources and warns that the water security situation in CA will worsen if no effective action is taken.

Resource Type

Journal Article

Publication Year

2022

Author

Wang, Xuanxuan; Chen, Yaning; Fang, Gonghuan; Li, Zhi; Liu, Yongchang

Language

English

University Affiliation

Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences, University of Chinese Academy of Sciences

Business Connect Takeaways

The use of artificial intelligence (AI) in water quality monitoring and management has increased in recent years due to advancements in technology and the need for more efficient and accurate monitoring systems.
AI techniques such as machine learning, neural networks, and fuzzy logic have been used to develop predictive models for water quality parameters such as pH, dissolved oxygen, and turbidity. These models can help identify potential water quality issues before they become a problem and allow for more targeted and efficient management strategies.
Despite the potential benefits of AI in water quality monitoring and management, there are still challenges that need to be addressed, such as the need for high-quality data, the lack of standardization in data collection and analysis, and the need for more research to validate the accuracy and effectiveness of AI models.

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