辽西北玉米干旱脆弱性评价模型构建与区划研究

Vulnerability evaluation and regionalization of drought disaster risk ofmaize in Northwestern Liaoning Province

  • 摘要: 本文基于IPCC(Intergovernmental Panel on Climate Change )报告中对气候变化背景下脆弱性的定义, 从暴露程度、敏感性、自身恢复能力和环境适应能力4个方面建立了干旱灾害脆弱性评价模型。以辽西北地区的玉米干旱灾害作为研究对象, 根据干旱灾害脆弱评价模型选取了气象因子、玉米生理因子以及经济社会因子等17项指标, 运用熵权法、加权综合评价法计算得到了辽西北地区玉米干旱脆弱性指数。为了验证和检验模型的适用性, 选取了辽西北1999年、2000年、2001年和2006年4个典型干旱年份, 将玉米干旱脆弱性指数划分为5个等级, 借助GIS技术绘制了玉米干旱脆弱性区划图。结果表明: 辽西北玉米干旱脆弱性强的区域主要集中在西部地区的阜新、朝阳、葫芦岛一带。重度以上脆弱性区域范围比例表现为2006年>1999年>2001年>2000年的规律; 其中, 2006年脆弱性最强, 影响面积最广, 造成的损失也最严重, 与实际灾情变化规律一致。分析2006年的玉米干旱脆弱性, 多数区域是由玉米生长季的降水异常引起的。通过对4个典型干旱年份的玉米干旱脆弱性指数与玉米减产率进行回归分析, 发现二者之间基本吻合, 通过了α=0.05的显著性F检验, 说明利用该模型对玉米干旱脆弱性的评价与区划是合理的, 可以用来评价和预测玉米干旱脆弱性、干旱灾害风险以及因干旱造成的玉米产量损失。研究结果可为当地农业干旱灾害风险评估和预警提供依据。

     

    Abstract: Based on IPCC definition of vulnerability within the context of climate change, a risk assessment model of maize vulnerability to drought was established from the perspectives of exposure, sensitivity and adaptability. Given drought disaster risk of maize in Northwestern Liaoning Province, 17 indexes related to crop physiological, meteorological and socio-economic factors were selected for the model simulation. The vulnerability indexes of maize to drought were calculated using the entropy and comprehensive weight evaluation methods. Four typical drought years (1999, 2000, 2001 and 2006) in Northeastern Liaoning Province were used to verify and test the applicability of the model. The maize vulnerability indexes to drought were divided into 5 grades on which basis maize vulnerability zone maps were isolated in GIS environment for typical drought years. The results showed that the areas with high drought vulnerability were mainly concentrated around Fuxin, Chaoyang and Huludao Counties. The areas with high vulnerability displayed a temporal regular pattern of 2006 > 1999 > 2001 > 2000. The level of drought vulnerability in 2006 was the highest, and had the most extensive impact area due to the precipitation anomaly in the growing season of that year. Regression analysis of maize vulnerability indexes to drought in the four typical drought years and maize yield loss was also conducted. The analysis showed a basic agreement among the factors for F test of significant at α=0.05. This indicated that it was reasonable to evaluate and predict the maize vulnerability to drought using the established model in the region. The model could be used to evaluate and predict maize vulnerability to drought, drought disaster risk and maize yield loss caused by drought. The results of this study strengthened further basis for local agricultural drought risk assessment and early warning.

     

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