基于CERES-Maize模型的吉林西部玉米干旱脆弱性评价与区划

Evaluation and regionalization of maize vulnerability to drought disaster in Western Jilin Province based on CERES-Maize model

  • 摘要: 自然灾害风险(risk)是灾害损失的可能性, 主要取决于致灾因子、脆弱性、暴露性以及防灾减灾能力4个因素。脆弱性(vulnerability)衡量承灾体遭受损害的程度, 是灾损估算和风险评估的重要环节, 是致灾因子与灾情联系的桥梁。在全面收集研究区气象、土壤、土地类型、田间管理数据等资料的基础上, 基于自然灾害风险和气候变化领域对脆弱性的定义, 在考虑到扰动、敏感性和适应能力的基础上建立干旱脆弱性评价模型。以吉林省西部的玉米干旱灾害作为研究对象, 选取2004年、2006年和2007年3个典型干旱年, 运用CERES-Maize模型逐日逐网格对玉米的生长过程进行模拟, 并且计算出不同生育期干旱脆弱性; 对相应年份的玉米因旱减产率与不同生育期脆弱性的相关分析表明, 二者存在指数相关性, 并且每个生育期都通过了a=0.05的F检验, 说明利用上述模型对玉米干旱脆弱性的评价与区划是合理的; 从相关系数的大小中可以看出, 玉米因旱减产损失与抽雄-乳熟期和拔节-抽雄期脆弱性相关性较大, 其次是乳熟-成熟期和出苗-拔节期。将不同生育期玉米干旱脆弱性指数划分为4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: 吉林省西部玉米干旱脆弱性较强的区域主要集中在白城、洮南、镇赉等地区, 玉米干旱脆弱性较弱的区域主要集中在松原、扶余等地区。运用此模型可以评价和预测玉米不同生育期干旱脆弱性以及因干旱造成的玉米产量损失, 本研究结果可以为研究区农业干旱灾害风险评估以及防灾减灾提供一定的依据。

     

    Abstract: Natural disaster risk is the possibility of disaster-related losses depending on hazard, vulnerability, exposure and emergency response and recovery capability. Vulnerability is the indicator measuring the damage extent of hazard-affected body. Vulnerability is not only an important part of risk assessment for loss estimation and disaster, but also the link between hazard-inducing factors and disaster. This study collected meteorological, soil, land use and field management data along with other related information on the research area to evaluate the vulnerability of maize drought disaster based on CERES-Maize model in western Jilin Province. According to the define of vulnerability of natural disaster risk and climate change, the study established the evaluation method of drought vulnerability based on disturbance, sensitivity and adaptive capacity. Data for three typical drought years (2004, 2006 and 2007) in western Jilin Province were used to calculate drought vulnerability using CERES-Maize model. Regression analysis was also conducted for maize yield losses caused by drought and vulnerability index in the three typical drought years for each growth period. The results showed exponential correlations between yield loss and vulnerability, which were significant at α = 0.05 (F test) for different growth stages. This indicated that it was reasonable to evaluate and predict maize vulnerability to drought using the es-tablished model in the region. Correlation coefficients indicated most close relationship between maize yield losses and vulnerability index at tasseling to milk-ripe stage and jointing to tasseling stage, and followed by milk-ripe to maturity stage and seeding to jointing stage. Drought vulnerability indexes of maize were divided into 4 grades and draught vulnerability zone maps of western Jilin Province drawn on GIS platform. The results showed that areas with high drought vulnerability included Baicheng, Taonan and Zhenlai. Low drought vulnerability areas included Songyuan and Fuyu. The established drought vulnerability evaluation model was suitable for evaluating and predicting drought vulnerability of maize at different growth stages and maize yield loss due to drought. The results of this study provided the basis for improving agricultural drought risk and emergency response and recovery capability.

     

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