| 基于主成分分析的广东省区域水资源紧缺风险评价 |
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| 引用本文： | 凌子燕,刘锐.基于主成分分析的广东省区域水资源紧缺风险评价[J].资源科学,2010,32(12):2324-2328. |
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| 作者姓名： | 凌子燕 刘锐 |
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| 作者单位： | 北京师范大学地理学与遥感科学学院,北京,100875 |
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| 基金项目： | 北京师范大学人才引进基金。 |
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| 摘 要： | 在调查和分析广东省水资源现状的基础上,选择水资源压力、社会状态和防旱响应3个准则层共18个指标构建水资源紧缺风险评价体系。运用主成分分析法确定评价指标权重后采用综合指数模型计算风险指数,应用GIS的空间分析技术,建立一套区域水资源紧缺风险评价指标体系和评价模型,为广东省水资源紧缺风险的研究和决策提供依据。评价广东省6个地区水资源紧缺风险的结果表明,农作物播种面积、工业废水排放达标率和万元GDP用水量3个指标对这些地区的水资源紧缺总风险指数的影响较大;各地区资源风险指数、使用风险指数和管理风险指数的差异体现出不同地区不同的缺水特征,参评地区中湛江已经进入了水资源紧缺高风险区域。
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| 关 键 词： | 水资源紧缺 风险评价 主成分分析指标权重 |
Risk Assessment on Regional Water Scarcity in Guangdong Province Based on Principal Component Analysis |
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| LING Ziyan and LIU Rui.Risk Assessment on Regional Water Scarcity in Guangdong Province Based on Principal Component Analysis[J].Resources Science,2010,32(12):2324-2328. |
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| Authors: | LING Ziyan and LIU Rui |
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| Institution: | School of Geography, Beijing Normal University, Beijing 100875, China;School of Geography, Beijing Normal University, Beijing 100875, China |
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| Abstract: | Guangdong province, located in the south of China, can generally be classified as the
tropical and semitropical zones with plentiful precipitation of an uneven distribution over space and
time. This study assesses the risk of water resources of cities of Guangzhou, Shenzhen, Zhuhai,
Shantou, Foshan, and Zhanjiang in Guangdong Province by analyzing data associated with water
resources, meteorology, demography, economy, as well as environmental protection. Three criteria
including water resources, social state, and response of drought control consisting of 18 indicators,
were adopted to build a risk assessment system. The principal component analysis method was
used to reasonably determine the weight of each indicator. The system is unique in simplifying the
data structure and synthesizing information reflected by a variety of data sources, largely obviating
the subjectivity involved in some traditional evaluation methods. The weighted indicators were
summed up as the risk indices (e.g., resource risk index, use risk index, management risk index,
and total risk index) to quantify the risk degree of water scarcity across the study cities. Results
showed that agricultural cultivation area, qualified rate of industrial sewage, and water
consumption of 10,000 Yuan GDP are the key indicators to determine the risk of the study areas.
This might be indicative of the risk of water scarcity being most affected by human activities.
Differences in resource risk index, use risk index, and management risk index among different
regions were likely to be as a result of varying degrees of drought severity for different cities.
Zhanjiang was found to be a high-risk area experiencing water scarcity, followed by Foshan and
Guangzhou. Shantou, Zhuhai, and Shenzhen showed relatively lower risks. It was also found that
in the principal component analysis method, the weight for each indicator is determined by the
indicator information and the system efficiency, suffering from less subjectivity. It can obviate the
case of the same weight for all assessment systems, which means that the same indicator in the
principal component analysis method can have different weights for varying regions. The study
provides a basis for risk assessment on regional water scarcity. As the risk would continue to
increase and even threat the sustainable development of society, it is suggested that governments at
all levels highlight the situation of water scarcity and raise the awareness of people on this issue.
Great effort should be made in the future to protect and efficiently utilize water resources. |
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| Keywords: | Principal component analysis Water scarcity Risk assessment Guangdong |
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