| 徐晗.基于熵权法的陕西省农业干旱脆弱性评价及影响因子识别[J].干旱地区农业研究,2016,34(3):198~205 |
| 基于熵权法的陕西省农业干旱脆弱性评价及影响因子识别 |
| Assessment of agricultural drought vulnerability and identification of influencing factors based on the entropy weight method |
| |
| DOI：10.7606/j.issn.1000-7601.2016.03.32 |
| 中文关键词: 农业干旱 脆弱性 空间差异 贡献度 陕西省 |
| 英文关键词:agriculture drought vulnerability Shaanxi Province spatial difference contribution degree |
| 基金项目:水利部公益性行业科研专项“渭河中下游干旱预警与应急水源配置（201301084）；陕西省教育厅科学研究计划项目（14JK1182） |
|
| 摘要点击次数: 1607 |
| 全文下载次数: 846 |
| 中文摘要: |
| 以陕西省10个城市为研究对象，从农业干旱的敏感性和恢复力2个角度选取14个指标，对全省的农业干旱脆弱性进行评价。利用熵权法和贡献度模型，根据陕西省2014年水资源、气象与社会经济统计数据，对10个城市的农业干旱脆弱性及主要贡献因子进行分析。结果表明：(1) 陕南的3个城市安康、商洛和汉中的农业干旱脆弱性水平较高，分别为0.7128、0.7110和0.5897，关中和陕北的7个城市农业干旱脆弱性处于中等水平；(2) 陕西省农业干旱脆弱性的空间差异与城市社会经济发展水平和气候条件不完全一致，陕南地区敏感性总体较高，其中最高为安康（0.4238），关中和陕北地区敏感性总体较低，其中最低为宝鸡（0.2123）；陕南地区三个城市的恢复力水平最低，关中和陕北对于干旱的恢复能力差异不大，其中咸阳（0.0992）、渭南（0.1301）、榆林（0.1554）3市的恢复力最强。(3) 从影响因子来看，西安、咸阳和渭南的主要影响因子为人口密度，贡献度分别为27.11%，15.11%和14.18%；安康、汉中和商洛的主要影响因子为旱地面积比重，贡献度分别为29.36%，17.20%和18.38%；延安和榆林的主要影响因子为旱地面积比重和耕地灌溉率，前者贡献度分别为32.18%和29.36%，后者贡献度均为17.24%；铜川的主要影响因子为耕地灌溉率，贡献度为16.49%；宝鸡的主要影响因子为水库调蓄率，贡献度为10.76%。最后，根据评价结果提出不同城市控制农业干旱脆弱性的相应措施。 |
| 英文摘要: |
| 10 cities in Shaanxi were selected as the research objects to evaluate the agriculture drought vulnerability in Shaanxi by 14 indicators through two points on the drought sensibility and resilience in agriculture. Entropy-value method and contribution model were adopted to assess the agriculture drought vulnerability and main contribution factors of the 10 cities in Shaanxi according to water resource, meteorology and social economy statistical data. The results showed that the agriculture drought vulnerability levels of Hanzhong, Ankang and Shangluo in southern Shaanxi were higher than others, reaching to 0.7128, 0.7110 and 0.5897, while the agriculture drought vulnerability levels of 7 other cities in Guanzhong and northern Shaanxi were medium. In addition, the spatial difference of agriculture drought vulnerability was not based on the social economy development level and land climate conditions in Shaanxi. The sensitivity was highest in Ankang (0.4238), lower in Guanzhong and northern Shaanxi, and lowest in Baoji (0.2123). The resilience level was lowest in southern Shaanxi, and few differences were found in Guanzhong and northern Shaanxi, while highest in Xianyang (0.0992), Weinan (0.1301) and Yulin (0.1554). For the impact factors, the main impact factor of Xi’an, Xianyang and Weinan was the population density with contribution degrees of 27.11%, 15.11% and 14.18%, respectively. The main impact factor of Ankang, Hanzhong and Shangluo was the dryland area proportion with contribution degrees of 29.36%, 17.20% and 18.38%, respectively. The main impact factor of Yan’an and Yulin was the dryland area proportion (contribution degrees of 32.18% and 29.36%) and the rate of irrigation (17.24% and 17.24%). The main impact factor of Tongchuan was the rate of irrigation with a contribution degree of 16.49%，and the main impact factor of Baoji was the rate of reservoir pondage with a contribution degree of 10.76%. Finally, this paper put forward measures of agriculture drought vulnerability controls in different cities based on the evaluation results. |
| 查看全文 查看/发表评论 下载PDF阅读器 |