基于DHSVM模型的作物渍害时空分布信息提取

为了实时监测作物渍害时空分布,以湖北省监利县作为研究对象,采用分布式水文土壤植被模型-DHSVM（Distributed Hydrology Soil Vegetation Model）将监利县分成若干90 m×90 m的栅格单元,通过计算每个栅格单元的水平衡,达到模拟农田土壤水分状况的目的,结合作物轻、中、重度3种渍害的水分特征指标（地下水位埋深小于0.6 m,土壤表层相对体积含水率5 d的均值大于90%的持续期分别大于5、12、20 d）的方法,模拟监利县夏收作物不同渍害的时空分布。结果表明,模拟得到了2014年、2015年每年1—5月（夏收作物生长期间）监利县农田表层土壤水分和地下水位埋...

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Bibliographic Details
Published inGuanʻgai paishui xuebao Vol. 36; no. 6; pp. 109 - 116
Main Author 熊勤学 田小海 朱建强
Format Journal Article
LanguageChinese
Published Xinxiang City Chinese Academy of Agricultural Sciences (CAAS) Farmland Irrigation Research Institute Editorial Office of Journal of Irrigation and Drainage 2017
Subjects
DHSVM模型
Groundwater
Groundwater levels
Growing season
Hydrology
Soil classification
Soil moisture
Spatial distribution
Summer
Temporal distribution
Temporal resolution
Vegetation
Water balance
Water table
Waterlogged ground
Waterlogging
时空分布
渍害
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ISSN1672-3317

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Summary:为了实时监测作物渍害时空分布,以湖北省监利县作为研究对象,采用分布式水文土壤植被模型-DHSVM（Distributed Hydrology Soil Vegetation Model）将监利县分成若干90 m×90 m的栅格单元,通过计算每个栅格单元的水平衡,达到模拟农田土壤水分状况的目的,结合作物轻、中、重度3种渍害的水分特征指标（地下水位埋深小于0.6 m,土壤表层相对体积含水率5 d的均值大于90%的持续期分别大于5、12、20 d）的方法,模拟监利县夏收作物不同渍害的时空分布。结果表明,模拟得到了2014年、2015年每年1—5月（夏收作物生长期间）监利县农田表层土壤水分和地下水位埋深的时空分布（时间分辨率为1 d,空间分辨率为90 m）,首次实现了作物渍害的时空间分布信息提取;通过多次田间调查,证明此反演方法结果是准确的。
Bibliography:We calculated the spatio-spatial distribution of waterloggingin Jianli County of Hubei province using the DHSVM model （The Distributed Hydrology Soil Vegetation Model）. The region in the county was divided in- to a number of grids, each being 90 m×90 m. The dynamic of soil moisture in each grid was simulated using a wa- ter balance model. We calculated the distribution of waterlogging in summer by classifying the waterlogging into three categories when the groundwater table was less than 0.6 m deep： when the duration of five-day average vol- umetric water higher than 90% lasted more than 5 days （light waterlogging）, or lasted more than 12 days （moder- ate waterlogging）, or lasted more than 20 days （severe waterlogging）. We simulated the distribution of soil mois- ture and groundwater table in all grids from January to May in 2014 and 2015 during the growing season of sum- mer crops using a temporal resolution of one day. These results enabled us to predict the spatio-temporal distribu- tion of waterlogging in
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ISSN:1672-3317