A thirst for development: mapping water stress using night-time stable lights as predictors of province-level water stress in China
| Autor: | Kyle M. Monahan, Xiaojun You |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
010504 meteorology & atmospheric sciences
Meteorology Endowment business.industry 0208 environmental biotechnology Geography Planning and Development Environmental resource management 02 engineering and technology 01 natural sciences Natural resource Population density Proxy (climate) 020801 environmental engineering Water resources Agriculture Linear regression Environmental science business China 0105 earth and related environmental sciences |
| Zdroj: | Area. 49:477-485 |
| ISSN: | 0004-0894 |
| Popis: | Given the rapid development within China, the inequality of available water resources has been increasingly of interest. Current methods for assessing water stress are inadequate for province-scale rapid monitoring. A more responsive indicator at a finer scale is needed to understand the distribution of water stress in China. This paper selected Defense Meteorological Satellite Program Operational Line-scan System night-time stable lights as a proxy for water stress at the province level in China from 2004 to 2012, as night-time lights are closely linked with population density, electricity consumption and other social, economic and environmental indicators associated with water stress. The linear regression results showed the intensity of night-time lights can serve as a predictive tool to assess water stress across provinces with an R2 from 0.797 to 0.854. The model worked especially well in some regions, such as East China, North China and South West China. Nonetheless, confounding factors interfered with the predictive relationship, including population density, level of economic development, natural resource endowment and industrial structures, etc. The model was not greatly improved by building a multi-variable linear regression including agricultural and industrial indicators. A straightforward predictor of water stress using remotely sensed data was developed. |
| Databáze: | OpenAIRE |
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