资源科学 ›› 2017, Vol. 39 ›› Issue (11): 2130-2140.doi: 10.18402/resci.2017.11.11

• 气候资源 • 上一篇    下一篇

基于乡镇尺度的黄土高原干旱脆弱性时空演变分析——以榆中县为例

石育中1, 李文龙1, 2, 鲁大铭1, 王子侨1, 杨新军1   

  1. 1. 西北大学城市与环境学院,西安710127;
    2. 内蒙古财经大学资源与环境经济学院,呼和浩特010070
  • 收稿日期:2017-04-10 修回日期:2017-08-05 出版日期:2017-11-20 发布日期:2017-11-20
  • 通讯作者: 杨新军,E-mail:yangxj@nwu.edu.cn
  • 作者简介:石育中,男,甘肃陇南人,博士生,研究方向为人地耦合系统脆弱性与区域可持续发展。E-mail:syz19880919@126.com
  • 基金资助:
    国家自然科学基金项目(41571163)

Spatio-temporal analysis of drought vulnerability on the Loess Plateau of China at town level

SHI Yuzhong1, LI Wenlong1, 2, LU Daming1, WANG Ziqiao1, YANG Xinjun1   

  1. 1. College of Urban and Environmental Sciences,Northwest University,Xi’an 710127,China;
    2. College of Resources and Environment Economy,Inner Mongolia Finance and Economics College,Hohhot 010070,China
  • Received:2017-04-10 Revised:2017-08-05 Online:2017-11-20 Published:2017-11-20

摘要: 干旱脆弱性评价是干旱半干旱地区人地关系研究的重要内容,对减缓和应对干旱具有重要作用,乡镇尺度的干旱脆弱性评价为西北乡村人地系统可持续性研究提供新的视角。本文引用干旱脆弱性分析框架,从暴露度、敏感性和适应能力三个维度构建指标体系,选择甘肃榆中县作为黄土高原典型研究区域,采用2002—2015年统计数据、气象数据和遥感数据,运用熵值法、综合指数法和局部空间自相关指数法等分析方法分别对指标权重、干旱脆弱性指数与类型及其空间集聚性进行分析。研究结果表明:①榆中县干旱脆弱性指数呈波动式变化趋势,阶段性升降明显;②不同脆弱类型的乡镇数量由高到低分别为中脆弱>高脆弱>低脆弱,且高脆弱的乡镇数量增加趋势明显;③干旱脆弱性影响因素由高到低分别为年降水量、坡度、年平均气温、干旱影响面积、农民纯收入、人口密度和农业人口比例,年降水量为干旱脆弱性的首要决定因素;④干旱脆弱性热点区域总体格局呈现“南-北热中部冷”的空间格局,具有明显的地理集聚特征,且局部乡镇热点区域趋于稳定。

关键词: Getis-Ord G*, 干旱脆弱性指数, 黄土高原, 时空演变, 影响因素

Abstract: Drought vulnerability assessment plays an important role in studies on mitigation and adaptation to combat drought. The assessment on the drought vulnerability of the rural areas at town level provides a new viewpoint of human-environment system sustainability in arid and semi-arid regions of northwestern China. In view of drought vulnerability assessment,we constructed an indicator system in three dimensions including exposure,sensitivity,and adaptive capacity,by adopting the comprehensive evaluation framework of drought vulnerability. We selected Yuzhong County,one of typical counties on the Loess Plateau,as the study area,and applied entropy evaluation method,drought vulnerability index,and the local spatial autocorrelation index to calculate indicator weights,the drought vulnerability index,and the spatial agglomeration respectively,based on statistical,meteorological and remote sensing data from 2002 to 2015 as model input. The main results include:(1)The drought vulnerability index varies greatly,with a clear rise or fall trend periodically from 2002 to 2015; (2)The number of towns with different drought vulnerability degrees ranging from high to low is middle,high,and low,and the number of towns with high drought vulnerability degree significantly increases;(3)The influential factors on drought vulnerability from high to low are annual precipitation,slope,annual mean air temperatures,drought area,net income of farmers,population density,and the proportion of agricultural population to total population,among which the annual precipitation is the main driver influencing the drought vulnerability; (4)In terms of spatial pattern of drought vulnerability,the hot spots are distributed in the south and north of Yuzhong County,while the cold spots are found in the central part of the county,indicating that there exists an obvious geographical agglomeration,and several towns in hot spot areas tend to be stable.

Key words: drought vulnerability index, Getis-Ord G*, influencing factors, Loess Plateau, spatial and temporal evolution