| 北京市水资源短缺风险等级评价与预测 |
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| 引用本文： | 廖强,张士锋,陈俊旭.北京市水资源短缺风险等级评价与预测[J].资源科学,2013,35(1):140-147. |
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| 作者姓名： | 廖强 张士锋 陈俊旭 |
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| 作者单位： | 1. 中国科学院地理科学与资源研究所,北京100101;中国科学院大学,北京100049
2. 中国科学院地理科学与资源研究所,北京,100101 |
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| 基金项目： | 国家自然科学基金项目(编号:41171032);国家重点基础研究发展计划(编号:2012CB955304)。 |
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| 摘 要： | 本文利用1979年-2009年的北京市水资源相关数据,采用灰色关联分析方法筛选出主要水资源短缺风险因子；用模糊聚类分析法对水资源系列进行聚类；用灰色系统GM(1,1)法预测2010年-2015年的水资源短缺风险,将结果进行模糊聚类分析.结果表明,降雨量、常住人口、第三产业及生活等其它用水量与北京市水资源短缺的关联度较大.在1999年之前,北京市水资源风险主要处于中等以及较低的等级.1999年之后,北京市的水资源风险基本处于较高的等级.在丰水年条件下,2010年-2015年北京市水资源风险处于较低等级；在平水年处于中等等级；在枯水年和极端枯水年,将处于较高风险甚至高风险等级.2014年后,南水北调工程引水入京将有效缓解水资源短缺状况,使水资源短缺的风险降低一个等级.
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| 关 键 词： | 水资源短缺风险 灰色关联分析 模糊聚类分析 灰色系统GM(1 1) 北京市 |
| 收稿时间： | 9/2/2012 12:00:00 AM |
Risk Assessment and Prediction of Water Shortages in Beijing |
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| LIAO Qiang,ZHANG Shifeng and CHEN Junxu.Risk Assessment and Prediction of Water Shortages in Beijing[J].Resources Science,2013,35(1):140-147. |
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| Authors: | LIAO Qiang ZHANG Shifeng and CHEN Junxu |
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| Institution: | Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;University of Chinese Academy of Sciences, Beijing 100049, China;Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China |
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| Abstract: | A shortage of water resources in Beijing has become a critical factor limiting its development. Many factors influence the risk of water shortage and because of their nature it is difficult to develop an accurate mathematical model to describe this risk. This study is based on water data from 1979 to 2009 for Beijing and uses grey relationship analysis to screen water shortage risk factors. Based on screened shortage risk factors, fuzzy cluster analysis was applied to cluster risk level and predict water shortage risk from 2010-2015. The results reveal the major risk factors as precipitation, population, service and domestic water, agricultural water, industrial water and percentage of waste water treatment. Prior to 1999, water shortage risk remained at a relatively low level, with fluctuations in high and low precipitation years. After 1999, risk has been consistently high. According to our predictive model, in high precipitation years, water shortage risk will be low, in normal precipitation years it will be medium, and in low or extremely low precipitation years the risk will be relatively high. Under the South-North Water Transfer Project, 0.5 billion cubic meters of water will be transferred to Beijing by September 2014. This will increase to one billion cubic meters by the end of 2015. It is clear that the South-North Water Transfer Project will play a key role in addressing the water crisis in Beijing. The results of our study are objective and provide a clearer understanding of the risk of water shortages in Beijing. These results will aid water resource planning, regional development planning and policy. |
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| Keywords: | Water shortage Risk Grey relationship analysis Fuzzy cluster analysis Grey model Beijing City |
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